JOT Spring 2011 Aramco

Spring 2011

THE SAUDI ARAMCO JOURNAL OF TECHNOLOGYA quarterly publication of the Saudi Arabian Oil Company

Giga-Cell Simulationsee page 2

Integrated Multiwell Formation Evaluationfor Diagnosing Reservoir Dynamicssee page 49

Journal of TechnologySaudi Aramco

Enhancement of PLT Reach in Horizontal Wells Using an Advanced Wireline TractorMuhammad H. Al-Buali, Alla A. Dashash, Ayedh M. Al-Sheri, Juan P. Torne and Walid Kamal Hussein

ABSTRACT

The horizontal wells drilled during the last few years are reaching a mature state in different fields and locations. Even existingvertical wells are being converted to horizontal wells for better oil sweep and productivity. This has created the need to evaluatethe production performance of horizontal wells to determine water source for proper monitoring and/or remedial action.

The Implication of Using State-of-the-Art Technologies in Oil FieldsKaram S. Al-Yateem, Dr. Fred Aminzadeh and Shiv N. Dasgupta

ABSTRACT

Petroleum, as a fuel and an inexpensive source of energy, has been one of the foundations of the Industrial Revolution and acause of growth for more than a century. The insatiable demand for oil and gas as a source of energy is expected to continuewell into the next century. Petroleum is also necessary for the production of many chemicals and consumer products. Tocontinue to meet the growth in demand for oil, the recovery from reservoirs in existing fields must be enhanced.

Evaluation of Sigma Phase Embrittlement of a Stainless Steel 304H Fluid Catalyst Cracking Unit Regenerator CycloneAli Y. Al-Kawaie and Abdelhak Kermid

ABSTRACT

Testing was performed on a 304H stainless steel sample removed from a fluid catalyst cracking unit (FCCU) regenerator cycloneafter 25 years of service to check for sigma phase formation. Sigma phase is a nonmagnetic inter-metallic phase composedmainly of iron and chromium (Fe-Cr), which forms in ferritic and austenitic stainless steels during exposure at the temperaturerange 1,050 °F to 1,800 °F (560 °C to 980 °C), causing loss of ductility and toughness.

Field Monitoring of an In-Service Thrust Anchor Block and PipelineDr. Ammar Khalil Abu Ghdaib, Dr. Muhammad K. Rahman, Aftab Ahmed, Shafiqur Rehman and Syed M. Shaahid

ABSTRACT

A research program was conducted to investigate the notion that the size of the concrete thrust anchor blocks used for cross-country hydrocarbon pipelines, designed per existing Saudi Aramco standards, can be reduced substantially. Field monitoring ofan in-service, large diameter hydrocarbon pipeline and an anchor block at a remote pig launching/receiving station was carriedout to investigate this aspect.

Assessing Plant Equipment Capability to Meet Forecasted DemandsDr. Salman T. Mishari

ABSTRACT

When plant equipment experiences a high failure rate or takes a long time to repair, concerns are often raised regarding itscapability to sustain its intended function and to meet forecasted demands. The question is how to address such concerns, and ifthey are found legitimate, what is the most cost-effective course of action? Is it replacement? Is it more equipment? Or is it to donothing and live with the situation as is?

Additional Content Available Online at: www.saudiaramco.com/jot

Spring 2011

THE SAUDI ARAMCO JOURNAL OF TECHNOLOGYA quarterly publication of the Saudi Arabian Oil CompanyJournal of Technology

Saudi Aramco

Contents

Giga-Cell Simulation 2Dr. Ali H. Dogru

First Successful Low-Cost Abrasive Perforation with WirelessAssisted Coiled Tubing in a Deviated High-Pressure/HighTemperature Gas Well 9Walter Nũnez-Garcia, J. Ricardo Solares, Jairo A. Leal Jauregui, Jorge E. Duarte, Alejandro Chacón, Robert Heidorn and Guillermo A. Izquierdo

Hydrocarbon Reservoirs Where Proactive Geosteering Is Most Likely to Succeed 18Douglas J. Seifert, Roland Chemali, Dr. Michael Bittar, Gary Althoffand Amr Lofty

Smart Combination of Technology Tools Resulted in a Successful Rigless Stimulation on a Trilateral Well: Case Study 25Ahmed K. Al-Zain, Abdulwafi A. Al-Gamber and Rifat Said

Inline Water Separation (IWS) Field Prototype Development and Testing 32Dr. Jinjiang J. Xiao, Ramsey White, Dr. Shoubo Wang and Dr. Luis E. Gomez

Analysis of Long-Term Production Performance in Acid Fractured Carbonate Wells 41Dr. Zillur Rahim, Mahbub S. Ahmed and Adnan A. Al-Kanaan

Integrated Multiwell Formation Evaluation for Diagnosing Reservoir Dynamics 49Dr. S. Mark Ma, Ali R. Al-Belowi, Mohammed A. Al-Mudhhi, Zaki A. Al-Ali and Dr. Murat M. Zeybek

Microbiologically Influenced Corrosion (MIC) Assessment in Crude Oil Pipelines 57Mazen A. Al-Saleh, Tawfiq M. Al-Ibrahim, Thomas Lundgaard, Dr. Peter F. Sanders, Dr. Ketil B. Sørensen and Dr. Susanne Juhler

Characterization of Corrosion Products in Saudi Aramco's Oil and Gas Facilities Using the X-ray Powder Diffraction Method 64Dr. Syed Rehan Zaidi, Dr. Husin Sitepu and Ahmed A. Al-Shehry

38661araD2R1.txt.qxp:ASC026 3/2/11 12:50 AM Page 1

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ABSTRACT

High resolution geological models built using seismic,geological and engineering data are often upscaled to smallersize flow models for reservoir simulation. It is well-knownthat reservoir heterogeneities play a very important role inreservoir behavior. Due to upscaling, these heterogeneities arelumped into average rock properties. Therefore, upscaledsimulation models only provide average reservoir performancerather than actual performance.

In general, vast amounts of seismic, geological andengineering data are available for the large reservoirs of theMiddle East. If high resolution seismic data is used, thesegeological models could be in the order of billions of cells. Inpractice, due to the limitations of reservoir simulators, thesehigh resolution models are often upscaled to flow models of afew million cells. This article presents a new parallel reservoirsimulator, GigaPOWERS™, which runs geological models withor without minimal upscaling. The world’s largest oilreservoirs with long production histories can now be simulatedusing over 1 billion cells in practical time spans. Thistechnology provides highly detailed pictures of the activityinside the reservoirs capturing the movement of fluids. It is avery powerful tool for designing new production scenarios torecover every last drop of oil using the most cost-effectivemeans.

INTRODUCTION

The world’s largest oil fields are located in the Middle East. SaudiAramco uses reservoir simulation technology to manage its vasthydrocarbon reservoirs and had relied almost entirely oncommercial simulators until 2000. Figure 1 shows the averagenumber of grid blocks (cells) used for the earlier black oilsimulation studies. As shown, the number of blocks started in thevicinity of 10,000 cells in 1988 and reached 150,000 by 2000.The average grid block size varied between 1 km and 0.25 kmwith the vertical number of layers ranging from 10 to 15. Thesewere highly upscaled models taken from the geological modelsthat contained several million cells and far more detail. This timeperiod is referred to as the “Kilo-Cell Simulation Period.”

In 2000, with the introduction of Saudi Aramco’s new, in-house parallel reservoir simulator, POWERS (Parallel Oil,

Water and Gas Enhanced Reservoir Simulator)1, the modelsize increased to over 1 million cells. The average grid cell sizewas less than 0.25 km, with a minimum of approximately0.08 km (80 m). The number of vertical layers also increasedfrom 10 to 100. These high resolution models were used inrelatively small fields, and they were proven to be moresuccessful for locating bypassed oil and guiding engineers todrill in specified locations to recover more oil1, 2. This timeperiod is the “Mega-Cell Simulation” period.

NEED FOR GIGA-CELL RESERVOIR MODELS

Many large fields were simulated over the past decade bymega-cell simulator technology, Fig. 2, and used the associatedpre- and post-processing capabilities during the past decade.The number of cells for the black oil simulation increased 30times during the past decade to 30 million cells.

This technology still lacked the capability to provide seismicscale simulation models for the world’s largest onshore andoffshore reservoirs. To simulate large reservoirs like GhawarArab-D at a seismic scale, a new technology — a new, morepowerful parallel reservoir simulator — was required.Simulating the world’s largest oil reservoir with sufficientresolution would have significant economic impact. With finegridding, high resolution models of large reservoirs can besimulated accounting for log scale and seismic scale hetero-geneities. This would mean a highly accurate reservoir

2 SPRING 2011 SAUDI ARAMCO JOURNAL OF TECHNOLOGY

Giga-Cell Simulation

Author: Dr. Ali H. Dogru

Fig. 1. Kilo-cell simulation.

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description that captures seismic and log scale heterogeneities,and therefore opens the door to designing new, more preciserecovery schemes to recover more oil.

Figure 3 illustrates the role of areal grid size when cap -turing reservoir heterogeneity. As shown, an areal square gridof 0.25 km (250 m) covers an area composed of two largeoffice buildings and a parking lot, walkways, trees andgarden. Such a large area is represented by a single dot whenused as a grid block, as illustrated by the square with a dot inthe middle in the top right corner of the figure. In terms ofreservoir simulation, this means that any rock heterogeneitieswithin this block are indistinguishable; all the heterogeneity isaveraged to a single value (e.g., one permeability value). Usingthis grid size would require 10 million cells to simulate theentire Ghawar Arab-D reservoir with 32 vertical layers.

If the grid block size is reduced to 0.08 km (80 m), the gridwill cover only about two office buildings. The grid blockfigure on the right shows that conceptually the block cancontain more information, i.e., a better image. If the areal gridsize is 0.08 km, 100 million cells would be required tosimulate the entire reservoir with the same number of verticallayers.

SAUDI ARAMCO JOURNAL OF TECHNOLOGY SPRING 2011 3

If the grid block size is further reduced to 0.025 km (25 m),which is equivalent to seismic scale, the image becomes muchclearer. Conceptually, this means that a 25 m simulation gridcan capture more reservoir heterogeneity. This would require1 billion cells to simulate the entire reservoir.

The next generation of POWERS, the new giga-cell parallelsimulator, GigaPOWERS, succeeded toward this end3. Thatjourney and the early trial of two giant reservoirs simulatedby an excess of 1 billion cells for the first time, covering theentire production history of 60 years and involving manyproducer and injector wells, has been documented4. Thereservoirs were simulated in seismic grid size (15 m to 42 m)within practical simulation times (15 hours to 32 hours).Technical issues were solved and smoothed out, and theofficial user version of GigaPOWERS was completed andlaunched in January 20103. The simulation time requiredcontinues to be reduced with new cluster computers and isexpected to decrease even further. The immediate target is toreduce it to eight hours – or an overnight run.

These examples and capabilities illustrate the early work ofthe “Giga-Cell Simulation” period. Figure 4 presents aprojection for the future model size in terms of black oil cells.

BENEFITS OF HIGH RESOLUTION SIMULATIONMODELS

Fine grid high resolution models result in two major types ofbenefits: (1) Reduced or eliminated upscaling, and (2) Highernumerical solution accuracy due to smaller grid size.

Benefits Due to Reduced or Eliminated Upscaling

Areal Resolution. As mentioned earlier, representing reservoirheterogeneity in reservoir models is essential. If this is notaccomplished and simulations are built on averaged reservoirproperties, the result produced by the simulator reflects anaveraged reservoir performance. Specifically, fluid movementbetween wells is largely misrepresented by the upscaledmodels. Although measured properties, such as well pressures,water cuts and gas-oil ratios (GORs), can be matched at wellswith the upscaled models, the distribution of oil and water

Fig. 2. Mega-cell simulation.

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Benefits Due to More Accurate Numerical Solution (Fine Grid)

An accurate numerical solution is important in calculating themovement of water and gas inside the reservoir. For example,when large grid blocks are used, pressure fields in the reservoircannot be accurately generated. This will affect the fluidvelocity fields, which are computed from the pressure solution.Large blocks will smear the advancing fluid fronts, oftenpredicting premature fluid breakthrough at the wells4 due to awell-known phenomenon called numerical dispersion. To matchthe water and gas arrivals at the wells, reservoir engineers oftenintroduce pseudo-relative permeabilities (and have to introducepermeability modifications), which could be unrealistic.

For example, for a 1 km well spacing, only one grid blockcan be placed between the wells if a 0.25 km sized grid blockis used. The pressure field calculated by this approach will notshow any sharp drop at the well cells, but instead averagedpressure values will be calculated for the three cells, Fig. 7.

On the other hand, if finer and finer grids are used, morecells can be placed between the wells, and therefore truepressure profiles between the wells can be captured that arecloser to the reality. The sharp pressure drop at the well cellscan be better computed by the fine grid model. For example,Fig. 7 shows that for a billion cell model of the largest oilfields, we can place nearly 40 grid blocks between the wells.This provides a higher numerical accuracy for the pressureand saturation solutions, and thereby well pressures, waterand gas breakthroughs can be calculated with accuracy.

EXAMPLES

Figure 8 illustrates that a 6 million cell simulation modelpredicts that a horizontal well located away from a gas capwill encounter gas breakthrough in 2 years. Consequently,when the model is refined, both in an areal and a verticaldirection, resulting in a 166 million cell model (with 28 timesfiner grids), Fig. 9, no gas breakthrough is calculated after 2years of production from the horizontal well. This exampleclearly demonstrates the effect of numerical dispersion.

within the reservoir (between the wells) cannot be matchedwith upscaled models unless the reservoir heterogeneity isproperly represented in the reservoir model.

Figure 5 shows that flow channels in the geological modeldisappear in the upscaled model using a 0.25 km areal grid.Obviously, the upscaled model cannot adequately simulate thefluid flow in channels specifically, and throughout the reservoirin general.

On the other hand, if the geological model can be simulatedintact, using as many cells as required to maintain theintegrity of all data, the resulting reservoir model wouldadequately capture fluid flow within the reservoir for moreaccurate reservoir management.

Vertical Resolution. High resolution, fine grid modelsshould have a sufficient number of vertical layers to capturevertical heterogeneity. Ideally, log scale layering2 in the orderof one foot, would capture all the vertical hetero geneity;however, this increases the computer time. Therefore, properupscaling can be applied to select layers while still capturingvertical heterogeneity. This is important in capturingadvancing water and gas fingers and similar fluid movementsinside the reservoir. Figure 6 shows an actual field case wherenear log scale vertical layering was selected.

Fig. 5. Areal view of geological model (left) and upscaled simulation model (right).

Fig. 6. Near log scale vertical layering. Fig. 7. More cells between the wells results in fine grid models.


Obviously if the simulator is fast, the 166 million cell modelshould be the choice for these reservoir studies.

The net effect on the well performance is presented in Fig.10 where the computed production rate (left axis) and GOR(right axis) are plotted against time for the two differentmodels. The production rates for the coarse grid (66 millioncells) and fine grid (166 million cells) are represented by blackand green colors, respectively. The GOR behaviors for thecoarse and fine grid models are represented by red and bluecolors. Figure 10 also shows that for the coarse grid modelproduction (black), the well is shut-in as the GOR (red)reaches a maximum (user specified criteria) near 2014. On theother hand, the fine grid production rate (green) goes to zero(well is shut-in) at 2016 when the GOR (blue) reaches amaximum value. When the two cases are compared, it is seenthat the fine grid model would yield an additional 2 years ofproduction at a higher rate than the coarse grid model.

Billion Cell Examples

GigaPOWERS3 has succeeded in simulating the world’s largestoffshore oil reservoir, Safaniya, in its entirety, with 1 billionand 8 million cells incorporating 60 years of production, Fig. 11. The areal grid size was 15 m on the oil-water system

reservoir in a run made on a cluster computer using 2,000cores and performed in only 15 hours.

The largest onshore oil reservoir, Ghawar, was also simulatedas a full-field model using 1 billion and 32 million active cells,Fig. 12a. This was a three-phase black oil simulation run. Again,using 4,000 cores of a cluster computer, the GigaPOWERSsimulator completed the run in 21 hours incorporating 60 yearsof production history involving many production and injectionwells. Total field water cut match for the first giga-cell run (priorto history matching) is shown in Fig. 12b.

Some Benefits of Billion Cell Models

Figures 13a and 13b compare the predicted water and oilsaturations for an area of Ghawar field under a given productionscenario at a given time. The image on the left represents themega-cell simulation model with a 0.25 km areal grid. On theright, the results of the giga-cell simulator with a 0.042 km gridfor the same reservoir at the same time are illustrated. The redcolor represents oil, and the blue color represents water.

Under the given production scenario, the mega-cellsimulator shows that water will move much faster, sweeping alarger area, leaving no oil behind.


Fig. 8. A coarse grid model with 6 million cells indicating a gas cap breakthroughat a horizontal well after 2 years of production.

Fig. 9. Fine grid model with 166 million cells showing no gas cap breakthrough atthe horizontal well after 2 years of production.

Fig. 10. Simulated well performance comparison using coarse grid (66 millioncells) shown in red and fine grid (166 million cells) shown in blue. The fine gridmodel reveals longer production time.

Fig. 11. Largest offshore oil reservoir in the world simulated in over 1 billion cells.



Therefore, in this example, giga-cell simulation presentsopportunities to recover more hydrocarbons.

Next Stages

Significantly more data will be available through onlinemeasurements and continuous monitoring. Voice recognitiontechnologies are becoming smarter at understanding spokencommands from people with different English language

The giga-cell simulation, however, reveals that there will beunswept zones, which will have oil remaining if this productionscenario is to be applied. This example illustrates the cleareffect of numerical dispersion for the mega-cell simulation,which is unrealistic. Giga-cell simulation shows less numericaldispersion. Based on the results of the giga-cell simulation,additional oil pockets indicated by the simulator should beproduced by in-fill drilling or sidetracking the wells.

Fig. 13a. Mega-cell simulation – Saturations. Fig. 13b. Giga-cell simulation – Saturations.

Fig. 12a. Giga-cell simulation of the largest onshore oil reservoir in the world. Fig. 12b. Water cut match of Ghawar field by GigaPOWERS.


accents. Better graphics technology will be available to rendermultibillion-cell images in real time. CPU technology will becomplemented with Graphical Processing Units (GPUs) for thecomputer intensive aspects of simulation and data analysisthat can benefit from parallel computation using hundreds ofcores in parallel.

Figure 14 presents a visionary prediction for the growth ofa black oil equivalent model size over the years. As shown,present computers can easily deliver teraflops (10**12 FLOPS(Floating Point Operations per Second)). Only large computercenters can deliver petaflops (10**15 FLOPS) today; however,with rapid development of GPUs and other evolving computertechnologies, eventually this will become available for smallersystems housed in the office. Again, with the currentmomentum, exaflop computers are on the horizon. All willimpact the size of the simulation models. Saudi Aramco soonwill be changing algorithms and rewriting new codes toachieve tera-cell reservoir simulation models with petaflop orexaflop computers.

SUMMARY AND CONCLUSIONS

Giga-cell reservoir simulation technology has been developedand implemented on the world’s largest offshore and onshoreoil reservoirs. Many cases have already successfully documentedthe high impact results that fine grid models yield, increasingthe understanding of the Saudi Arabian reservoirs.

Giga-cell simulation technology reveals the crucial detailsthat enable engineers and geoscientists to build, run andanalyze highly detailed oil and gas reservoir models with greataccuracy, which will help companies recover additional oiland gas. Overall, giga-cell simulation is expected to bebeneficial for mankind in its quest to produce morehydrocarbons to sustain the world’s economic development.


ACKNOWLEDGMENTS

The author would like to thank Saudi Aramco managementfor their continued high level of support in developing themany aspects of this technology. The author would also liketo thank the members of the EXPEC ARC ComputationalModeling Technology Team who contributed to this article:Usuf Middya, Larry Fung and Tareq Al-Shaalan, as well asHeather Bence for organizing and editing the text.

A shorter version of this article titled “Giga-Cell SimulationImproves Recovery from Giant Fields,” was previouslypublished in World Oil, Vol. 231, No. 10, October 2010, pp. 65-70.

REFERENCES

1. Dogru, A.H., Sunaidi, H.A., Fung, L.S.K., Habiballah,W.A., Al-Zamel, N. and Li, K.G.: “A Parallel ReservoirSimulator for Large-Scale Reservoir Simulation,” SPEReservoir Evaluation & Engineering Journal, Vol. 5, No. 1,February 2002, pp. 11-23.

2. Pavlas, E.J.: “MPP Simulation of Complex WaterEncroachment in a Large Carbonate Reservoir,” SPE paper71628, presented at the SPE Annual Technical Conference& Exhibition, New Orleans, Louisiana, September 30 -October 3, 2001.

3. Dogru, A.H., Fung, L.S.K., Middya, U., et al.: “A NextGeneration Parallel Reservoir Simulator for GiantReservoirs,” SPE paper 119272, presented at the SPEReservoir Simulation Symposium, The Woodlands, Texas,February 2-4, 2009.

4. Dogru, A.H., Fung, L.S.K., Shaalan, T.M., Middya, U. andPita, J.A.: “From Mega to Giga-Cell Simulation,” SPEpaper 116675, presented at the SPE Annual TechnicalConference and Exhibition, Denver, Colorado, September21-24, 2008.

Fig. 14. Past and predicted growth trend of model size with increasingcomputational speed.



BIOGRAPHY

Dr. Ali H. Dogru is Chief Technologistof the Computational ModelingTechnology Team at the Explorationand Petroleum Engineering Center -Advanced Research Center (EXPECARC). He is in charge of supervisingscientists and engineers developing

Saudi Aramco’s parallel reservoir simulator, POWERS, nowGigaPOWERS. Formerly, Ali was General Supervisor of theTechnology Development Division, involved in reservoirsimulator development and production technology.

He joined Saudi Aramco in 1988, on loan from theMobil Oil Company in Dallas, TX. His former industrialexperience includes Mobil Oil and Core Labs/EngineeringNumerics Corp., Dallas, TX.

Ali has a Ph.D. degree in Petroleum Engineering with aminor in Applied Mathematics from the University ofTexas at Austin, Austin, TX. His academic experienceincludes various positions at the University of Texas in theDepartment of Mechanical Engineering and Mathematics,at the California Institute of Technology in ChemicalEngineering, at the Norwegian Institute of Technology andat the Technical University of Istanbul.

Ali shares four U.S. patents and is the author of over 40technical publications.

He is the recipient of the 2008 SPE InternationalReservoir Description and Dynamics Award and the 2010World Oil Innovative Thinking Award. The project that heleads, GigaPOWERS, received the 2010 ADIPEC BestTechnology Award.


ABSTRACT

A number of highly deviated gas producing wells in thesouthern area of the Ghawar field in Saudi Arabia wererecently perforated using coiled tubing (CT) conveyed guns,and after performing several jobs, it was found that the costof using the coiled tubing conveyed perforating (CT-TCP)approach was significantly higher than anticipated. Therefore,it became apparent that more cost-effective options wererequired. The decision was then made to trial test the CTabrasive hydrajetting perforating (AHP) technique.

A well with a gross pay thickness of 130 ft was selectedfor the first trial. Consideration was given to the fact that thechance of achieving the job objective is higher if the selectedintervals to perforate and the number of slots that can bemade in the formation in one run are not excessive.Therefore, it was decided to create slots in 12 differentsections along the wellbore. Given this constraint, it wasimportant to achieve an optimum depth correlation, whichwas done by using a wireless real-time casing collar locator(CCL) tool, the first time for its use in this type of operationin Saudi Arabia. A procedure was designed to optimizeimplementation time and chemical volumes, and the job wassuccessfully performed with better than expected results.

The objective of this article is to share results, lessonslearned, and solutions used to overcome problems and achievethe successful implementation of a technique offering a valid,low-cost alternative to conventional perforating in a highlydeviated well. Full details of the planning and design of the job,operational procedures and data collected are also provided.

INTRODUCTION

As shown in several technical manuals and papers dating backto the late 1950s, the abrasive hydrajetting perforating (AHP)technique was tested and proven to be an approach to connectthe reservoir that induced zero damage, in contrast withconven tional perforating techniques using explosives1-3.Unfortunately, the AHP technique has not been widely useduntil recently, due to concerns about longer operating times andits perceived overall inferior operational practicality; however,use of the technique has gained momentum in certain areasaround the world, in large part because of its cost effectivenessand excellent results.

The technique has been widely used to increase the fluidentry area in wells scheduled for hydraulic fracturingstimulation as described in various publications1, 2. Thetechnique has also been used as a way to overcome logisticdifficulties in regions where permits to store and transportexplosives are difficult to obtain.

One of the distinctive advantages of using AHP forstimulation applications is the reduction of near wellboretortuosity due to the large hole sizes created in the casingand in the formation rock. As mentioned3, tortuosity willoften restrict the flow of hydrocarbons from the formationto the wellbore and restrict fluid entry during hydraulicfracturing treatments. Another distinct advantage of AHP isthat the crushed rock and metal debris generated from theuse of shaped charges during conventional perforating doesnot occur, thereby eliminating the usual reduction in theoverall production potential. Only minimal skin is createdwhen using AHP, because the formation rock is removedwith the abrasive slurry instead of being crushed by anexplosive charge3.

These advantages, and the results of a cost comparisonstudy, which showed that AHP was 20% more cost-effectivethan the traditional coiled tubing (CT) perforating approachbeing used, provided the necessary incentive to proceed with afield trial in Well A, a highly deviated cased well that is a high-pressure/high temperature (HP/HT) gas producer.


First Successful Low-Cost AbrasivePerforation with Wireless Assisted CoiledTubing in a Deviated High-Pressure/HighTemperature Gas WellAuthors: Walter Núñez-Garcia, J. Ricardo Solares, Jairo A. Leal Jauregui, Jorge E. Duarte, Alejandro Chacón, Robert Heidorn and Guillermo A. Izquierdo

Fig. 1. Selected perforating zones (14,770 ft to 14,900 ft) in Well A.


JOB DESIGN

Based on the experience gained from selective stimulationtreatments performed in packerless open hole wells in thesame field, the maximum number of abrasive slurry stagesthat can be carried out before washing out the hydrajettingtool was determined to be 12 to 14. Consequently, 12 highporosity zones were selected for perforation in a 130 ft


Fig. 3. The average calculated total penetration of AHP beyond cement and pipewall thickness.

Fig. 4. Abrasive perforation average length.

Fig. 5. Abrasive perforation average diameter.

Fig. 6. Abrasive perforation average diameter.

Fig. 7. Jetting tool with three 3⁄16" nozzles.

(14,770 ft to 14,900 ft) gross interval with a 73° deviation inWell A. Figure 1 shows the 12 selected zones.

It was decided to generate three perforations per interval,for a total of 36 perforations, which was less than thetraditional six shots per foot perforation density designed tomaximize well productivity when using conventionalperforating guns. Several studies1, 3 have shown that becauseof the reduction in tortuosity and crushing damage, the lowerAHP density is equivalent to the higher perforating density ofconventional guns. The pictures in Fig. 2 show the averageshape of abrasive hydrajetting generated perforations, whichachieve a much larger contact area than conventionalperforation tunnels without any crush zone.

The average calculated total penetration of AHP beyondthe cement and pipe wall thickness is 3.72”, Fig. 3. This valuecorresponds to the minimum expected penetration based on

Fig. 2. The average shape of abrasive jetting generated perforations.


empirical correlations from surface tests. At downholeconditions, this figure is highly likely to increase due to theeffect of hydrostatic pressure and the hydrajetting stagnationpressure that microfractures generate4-6, making the abrasivejet go deeper than expected into the formation.

From empirical data and yard tests, it was possible toestimate that at surface conditions the average penetrationlength in Well A would be approximately 5½”, or even deeperafter pumping for approximately nine minutes with a ΔP of ~2,500 psi. The abrasive slurry used during the yard tests wasthe same that was used during the actual operation. Figures 4to 6 show the results obtained during the yard test performedahead of the main job.

The jetting tool for the planned job in Well A was con figuredso as to obtain the desired pressure drop across the nozzles,

taking into account the restrictions imposed by the 2” CT to beused. The previous experience in open hole stimulationtreatments in the area suggested that the best approach was toinstall three coplanar 3⁄16” nozzles set 120° apart. This is themaximum number of nozzles that can be installed in the tool tobe able to generate between 2,000 psi to 3,000 psi with theflow restriction imposed by a 2” CT. Figure 7 shows the jettingtool with the aforementioned nozzle configuration.

Given that only 12 zones were selected for perforating witha total of 36 holes, it was deemed necessary to achieve highdepth precision at the moment of positioning the jetting tool.Installing memory gauges was first considered, but havingreal-time correlation was determined to be important, so thedecision was made to use a 3½” wireless casing collar locator(CCL) tool that could tolerate the abrasive slurry while


Fig. 8a. Decision Tree.

Start

Rig up 2” conventional HPCT, pumping unit

and N2 unit

Clean the wellbore all the way to TD and

perform bottoms -up

RIH with GR/CCL memory gauges to perform

correlation passes (with and without pressure to

verify CT stretch)

Perform dedicated run for wellbore

cleanout Is there access

to the perforating zone?

Rig down and cancel the job

Is there access to the perforating

zone?

Yes

RIH CT with jetting tool and wireless CCL (depth correlation) for abrasive

jetting perforation

No


zone?


zone?

Execute hydrajetting

perforations as planned

Yes

No

Perform necessary runs to complete 12 abrasive

jetting perforation stages

POOH CT and rig down equipment Flow back

No

No

Yes

Go to Page 2

Circulate 50-100 bbls of CT drag friction reducer

Circulate 50-100 bbls of CT drag friction reducer


zone?

Yes

POOH CT breakdown BHA and install memory

gauges

Execute correlation run (memory gauges + wireless CCL) and POOH CT to surface

to change BHA Yes

Make up motor and mill and RIH to mill

out obstruction No


zone? Yes

Rig down and cancel the job

No

POOH CT to 13,800 ft and wait for sand to settle

(3 hours)

RIH with jetting tool and tag the top of

the fill . Sand covering perforations? Yes

No

POOH, M/U SCO BHA and flow

cleanout procedure as per Attachment #13

Run 3½” GC down to max depth


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providing a sufficiently large flow path to allow pumping at~3.0 barrels per minute (bpm).

Then, the operational decision tree, Figs. 8a and 8b, wasdeveloped to prepare for a wide spectrum of possiblescenarios. The main decision tree branch included a requiredwellbore cleanout to ensure full access to the perforating zone,and to try to minimize potential sticking problems whenrunning in hole (RIH) with the final bottom-hole assembly(BHA), Fig. 9. The cleanout procedure included a nitrifiedfluid combined with linear gel to have excess annular velocityin case a large amount of debris was found in the wellbore. Anitrogen (N2) kickoff contingency was included in the plan incase the well could not flow by itself. A contingent acidmatrix treatment was also included in case the well did notachieve its production target.

JOB IMPLEMENTATION

A base cased hole log was not available for Well A, so gammaray/CCL memory gauges were run in tandem with the wirelessCCL as a backup in case something went wrong. Logging

Fig. 8b. Decision Tree.

Does the well flow by itself?

N2 lifting

No

Yes Page 2

Is formation water being produced?

End Job Rig down and prepare a

program for PLT, surveillance and water

shut-off

Does the well flow by itself?

Yes Yes

No No

Bullheading matrixacidizing

Flow back and test the well

Is the well producing as expected?

No

End Job

Yes

Fig. 9. CT BHA.


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final AHP run. The CT was run down to the perforatinginterval and a final correlation pass was made beforepositioning the jetting tool in front of the lowermost zone to beperforated. Figure 10 shows the comparison between the twoCCL correlations, one using memory gauges and the other oneusing the CT run wireless CCL, which performed very well.

A 2,000 gallon abrasive slurry mixture of 40#/Mgal lineargel and 100 mesh sand with a 0.5 ppa concentration waspumped at an average rate of 2.5 bbl/min to 3.0 bbl/min foreach perforating stage. The maximum pumping rate wasdriven by the maximum allowable circulation pressure, whichwas set at 9,000 psi due to safety considerations. A hydrogensulfide corrosion inhibitor pill was pumped after each stage toprotect the CT and BHA.

Figure 11 shows data details of the operation to completeall 12 scheduled stages. Each stage can be clearly distinguished


Fig. 11. Acquisition graph from abrasive jetting perforation stages.

Fig. 12. Data recorded during injection test and acid wash operations.

Fig. 10. Compararison between the two CCL correlations, one using memorygauges and the other one using the CT run wireless CCL.

passes were made at the speed required for the wireless CCLtool to work properly, while pumping at a minimum rate, andthen the pumping rate was increased to determine the CTstretch. A different set of logging passes at a higher speed weremade at different pump rates to determine the CT stretchduring the AHP operation. This logging run was equivalent torunning a base cased hole log, which could then be used forfuture interventions requiring depth correlation. Aftercompleting the correlation logging run, the jetting tool wasmade up and run in tandem with the wireless CCL during the

Table 1. Production data pre- and post-stimulation

Choke FWHP BS&W Estimated TCASize (psi) (%) Gas Rate (psi)

(MMscfd)Post-stimulation 46/64” 2,695 4 22 2,831Pre-stimulation 26/64” 985 10 3 900


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CONCLUSIONS

1. The successful implementation of the field trial demonstratedthat hydrajetting perforating is a viable and safe alternativeto conventional perforating techniques. The post-stimulationgas rates from Well A exceeded all expectations.

2. The use of a wireless CCL tool proved to be a key to thesuccessful outcome of the operation. The depth correlationperformed was accurate, and the tool worked well underabrasive conditions.

3. The 100 mesh sand worked well as the abrasive materialneeded for the hydrajetting perforating operation; however,the small size of the material created a low permeabilitypack around the perforations, which reduced injectivityand required an acid wash. Similar jobs performed in otherwells after this one have been carried out using 20/40proppant as the abrasive material with much better resultsand no operational complications.

4. Due to concerns about the back splash effect that theabrasive material could have on the jetting tool, thenumber of stages was limited to 12, which proved to beconservative. The jetting tool withstood the abrasive effectof 12,000 lbs of 100 mesh sand without any problems orapparent damage, thereby indicating that future jobs canbe designed with a larger number of stages.

5. The data obtained from the downhole pressure/temperaturegauges set below the jetting tool, which were configured tomeasure data from inside the CT and from the CT casingannulus, were very valuable. The collected data helpedanalyze the effective pressure drop across the nozzle, whichis the main point in achieving penetration through theformation. Figure 13 shows surface and downhole pressuredata recorded throughout the operation.

6. The acid wash performed right after completing the hydra -jetting perforating operation was useful in increasingconductivity and helping the well flow back on its own.

7. A wellbore cleanout ahead of the hydrajetting perforatingoperation is highly recommended.

ACKNOWLEDGMENTS

The authors would like to thank the management of SaudiAramco and Halliburton for their support and permission topublish this article.

This article was prepared for presentation at the Abu DhabiInternational Petroleum Exhibition and Conference, AbuDhabi, U.A.E., November 1-4, 2010.

by observing the pressure spike recorded every time abrasiveslurry was pumped through the CT. The CT pressure at thesurface was ~6,300 psi throughout the operation whilepumping at 3 bpm. The flowing wellhead pressure when thechoke was fully open, which was done to circulate abrasivesand out of the wellbore and avoid clogging the BHA, wasaround 600 psi.

After completing the perforating stages, the CT was pulledup to 14,000 ft to allow for settling time, and after threehours the CT was rigged in hole again to verify that theperforations were not plugged. The CT did not tag hard, so itwas concluded that the perforations were free of obstructionin the wellbore. The CT was then pulled out of the hole and anitrogen lift was performed to kickoff the well. The wellflowed on its own at below target rate so an injectivity testahead of bullheading a matrix stimulation treatment wasattempted, but it was unsuccessful as the injection pressurebuilt up quickly and no fluid intake was observed. It wasassumed then that the most likely cause of the problem wasthat the perforations had been plugged up, either with the 100mesh sand used in the abrasive slurry during perforatingoperations or with some other solid material. An acid washwas successfully performed using a high-pressure CT to pumporganic and 15% hydrochloric acid blends. A new injectivitytest was then attempted, and the injectivity rate significantlyincreased from 0.8 bpm to 6 bpm. Figure 12 shows details ofthe operation.

Finally, a matrix acid treatment was bullheaded down thetubing at an initial maximum treating pressure and rate of6,000 psi and 5 bpm, respectively. The well was opened forflow back at an initial shut-in wellhead pressure of 3,580 psi,and it performed in an excellent manner, Table 1.

This job was the first successful utilization of hydrajettingtechnology as a cost-effective alternative to perforating withCT in a high angle Saudi Aramco gas producer. The resultsfrom this highly successful field trial were very encouraging,and the technology has since been used with equal positiveresults in other wells.

Fig. 13. Merged surface and downhole pressure data from gauges.


REFERENCES

1. McDaniel, B.W., Surjaatmadja, J.B. and East Jr., L.E.: “Useof Hydrajet Perforating to Improve Fracturing Success SeesGlobal Expansion,” SPE paper 114695, presented at theCIPC/SPE Gas Technology Symposium 2008 JointConference, Calgary, Alberta, Canada, June 16-19, 2008.

2. Rees, M.J., Khallad, A., Cheng, A., Rispler, K.A.,Surjaatmadja, J.B. and McDaniel, B.W.: “SuccessfulHydrajet Acid Squeeze and Multifracture Acid Treatmentsin Horizontal Open Holes Using Dynamic DiversionProcess and/or Downhole Mixing,” SPE paper 71692,presented at the SPE Annual Technical Conference andExhibition, New Orleans, Louisiana, October 1-3, 2001.

3. Surjaatmadja, J.B., Abass, H.H. and Brumley, J.L.:“Elimination of Near-Wellbore Tortuosities by Means ofHydrojetting,” SPE paper 28761, presented at the AsiaPacific Oil & Gas Conference, Melbourne, Australia,November 7-10, 1994.

4. Surjaatmadja, J.B. and Sierra, L.: “New Alternative toSelectively Fracture Stimulate Extended Reach, HorizontalWells,” SPE paper 119475, presented at the SPE MiddleEast Oil & Gas Show and Conference, Manama, Bahrain,March 15-18, 2009.

5. Garzon, F.O., Franco, C.A., Ginest, N.H., Sierra, L.,Surjaatmadja, J.B. and Izquierdo, G.: “First SuccessfulSelective Stimulation with Coiled Tubing, HydrajettingTool, and New Isolation Sleeve in an Open Hole DualLateral Well Completed in a Saudi Arabia CarbonateFormation: A Case History,” SPE paper 130512, presentedat the SPE/ICoTA Coiled Tubing and Well InterventionConference and Exhibition, The Woodlands, Texas, March23-24, 2010.

6. Garzon, F.O., Franco, C.A., Al-Saeed, H.A., Al-Omair,W.M. and Ginest, N.H.: “Successful Selective Stimulationof Open Hole Dual Lateral Gas-Condensate Producerswith a Coiled Tubing, Hydra Jetting Tool and NewIsolation Sleeve in Saudi Arabia,” SPE paper KSA-0138,presented at the SPE/DGS Annual Technical Symposiumand Exhibition, al-Khobar, Saudi Arabia, April 4-7, 2010.




Jairo A. Leal Jauregui is a SeniorPetroleum Engineer in the GasProduction Engineering Division of theSouthern Area Production EngineeringDepartment (SAPED). He has 19 yearsof experience in the oil and gasindustry in areas like workovers, acid

stimulation, and perforating and fracturing, with operationsin Colombia, Venezuela, Argentina and Saudi Arabia. Jairohas authored several Society of Petroleum Engineers (SPE)papers on field technology applications, fluids andstimulation results.

In 1990, Jairo received his B.S. degree in PetroleumEngineering from the Universidad Industrial de Santander,Bucaramanga, Colombia, and a Specialization in ProjectManagement from Pontifica Universidad Javeriana, Bogotá,Colombia, in 2005.

Jorge E. Duarte is a ProductionEngineer working in the GasProduction Engineering Division. Hehas 13 years of oil field experience.

In 1996, Jorge received his B.S.degree in Petroleum Engineering fromthe Universidad America, Bogotá,

Colombia.

Alejandro Chacón is the LeadTechnical Engineer for HalliburtonCoiled Tubing in Saudi Arabia. He hasheld this position since January 2009.Alejandro joined the industry in early2006 in Colombia as a Field Engineer,and since then he has gained

experience in the following types of operations, amongothers: matrix stimulation, pinpoint stimulation, logging,CT-TCP, conformance and general coiled tubing (CT)extended reach applications.

He is currently focusing on new technology applicationsfor CT operations in Saudi Arabia.

In 2006, Alejandro received his B.S. degree inMechanical Engineering from the Universidad de los Andes,Bogotá, Colombia.

BIOGRAPHIES

Walter Nũnez-Garcia is a SeniorPetroleum Engineer. He has worked forthe Gas Production EngineeringDivision for 4 years and has 17 yearsof overall experience in the oil industry.Previously, Walter worked forECOPETROL (Colombian national oil

company) serving in several different technical andadministrative positions.

In 1992, he received his B.S. degree in PetroleumEngineering from the Universidad America, Bogotá,Colombia, and in 2000, he earned a financial degree fromLa Gran Colombia University, Bogotá, Colombia. Walterthen earned his M.S. degree in Petroleum Engineering fromthe University of Oklahoma, Norman, OK, in 2002.

He is a member of the Society of Petroleum Engineers(SPE).

Walter has authored several SPE papers covering fieldtechnology applicstions.

J. Ricardo Solares is a PetroleumEngineering Consultant and aSupervisor with the Southern AreaProduction Engineering Department(SAPED) in ‘Udhailiyah. He has 25years of diversified oil industryexperience. Throughout his career,

Ricardo has held positions as a Reservoir and ProductionEngineer with Arco Oil and Gas and BP Exploration, whileworking in a variety of carbonate and sandstone reservoirslocated throughout the world’s major hydrocarbonprovinces in the Middle East, the Gulf of Mexico, Alaskaand South America.

Since joining Saudi Aramco in 1999, he has beeninvolved with a variety of technical projects and planningactivities that are part of large gas development projects.Ricardo manages a team responsible for the introductionand implementation of new technology, the issuing ofoperating standards, stimulation and productionoptimization activities, and completion design.

His areas of expertise include hydraulic fracturing andwell stimulation, all aspects of production optimization,completions and artificial lift design, pressure transient andinflow performance analysis, completions design andeconomic evaluation.

In 1982 Ricardo received his B.S. degree in GeologicalEngineering and in 1983 he received his M.S. degree inPetroleum Engineering, both from the University of Texas atAustin, Austin, TX. He also received an MBA in Financefrom Alaska Pacific University, Anchorage, AK, in 1990.

Ricardo received the 2006 Society of PetroleumEngineers (SPE) Regional Award in the area of Managementand Information, and a SPE Technical Editor award for hiswork on the Editorial Review Committee. He has alsopublished over 20 SPE papers and articles in a variety ofinternational technical publications.


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Robert Heidorn is the Lead TechnicalProfessional for Halliburton CoiledTubing, Saudi Arabia, and has held thisposition since December 2009. Hejoined Halliburton in early 2006 inSouth Texas, where he received hiscoiled tubing (CT) training, and then

worked for the following 3 years offshore in the Gulf ofMexico, before transferring to Saudi Arabia. During thistime as a Field Engineer, Robert gained experience information consolidation, matrix stimulation, pinpointstimulation, and general CT operations for high deviationand extended reach applications, among many others.

In 2006, he received his B.S. degree in MechanicalEngineering from Louisiana State University, Baton Rouge, LA.

Guillermo A. Izquierdo is a PetroleumEngineer working for Halliburton inSaudi Arabia as a Senior AccountRepresentative in ProductionEnhancement. He has held this positionsince December 2005. Guillermo joinedHalliburton in 1997 and his experience

includes acidizing, de-scaling, scale inhibition, coiledtubing, conformance and fracturing technology applicationsfor both sandstone and carbonate formations.

He received his B.S. degree in Petroleum Engineeringfrom the Universidad Industrial de Santander, Bucaramanga,Colombia, in 1996.

Guillermo has authored several papers coveringstimulation technology. He is currently focused on newtechnology applications for production enhancement forSaudi Arabian fields.


ABSTRACT

Proactive geosteering is the fastest growing form ofgeosteering. It enables reservoir exits to be anticipated beforethey occur and well paths to be altered to navigate within thebest portion of the reservoir. This article demonstrates thatproactive geosteering when utilizing resistivity tools is mostlikely to succeed when drilling in a high resistivity reservoirsurrounded by low resistivity formations. The likelihood ofsuccess is further improved when the different media areseparated by sharp boundaries, whereas gradationaltransitions prove to be challenging.

These observations are substantiated and quantified withmodels and actual case histories. The key enabling geosteeringtechnology considered in this study is the newly deployedazimuthal deep resistivity family of logging while drilling(LWD) sensors. These sensors can detect and locate anapproaching boundary from several feet away. Geosteeringpersonnel track oil-water contacts (OWCs) and shale/sandinterfaces. The earlier these boundaries are identified, thebetter the chances for successful geosteering. The resistivitiesof the formations are important because the lateral reach ofthe sensor is most favorable when navigating in a highresistivity reservoir adjacent to low resistivity formations.Similarly, the least favorable situations occur when navigatingin a low resistivity interval and seeking a high resistivityformation. A series of modeling results help to quantify andvisualize the expected geosteering performance for variousresistivity values in the reservoir and approaching formations.

This article includes case studies from actual geosteered

wells to illustrate the performance predicted by the theoreticalmodels. It also provides guidelines for planning and inter -preting a proactive geosteering operation.

INTRODUCTION

In conventional reservoirs, the geosteering of high angle wellsis often practiced to optimize initial production, and tomaximize cumulative hydrocarbon recovery through the life ofthe field. Figure 1 shows an arrangement between injectorwells and producing wells. As is customary, an injector well,Well C, is placed in the lower section in the reservoir, in thiscase in the water bearing interval. For illustration purposes,two producing wells are placed in the oil interval. The upperwell, Well A, is close to the roof and the second well, Well B,is closer to the oil-water contact (OWC). Initially, both wellsare likely to perform equally. As the reservoir is depleted, theOWC rises and the proximity of the water to Well B is likelyto cause water coning. As the water level continues to rise,Well B will cease to produce meaningful quantities of oil,while Well A will contribute significantly more. Over the lifeof the field, a properly placed horizontal well is likely toproduce significantly more oil than a well poorly located inthe reservoir.

Properly placing a well often involves keeping it within adesired distance from a series of geological events – typicallyOWCs, reservoir roofs or floors or nearby shale lenses. This isnormally achieved through geosteering1. Multiple methods forgeosteering have been described in the literature, but to keep thewell at all times within the confines of the reservoir and to avoid


Hydrocarbon Reservoirs Where ProactiveGeosteering Is Most Likely to Succeed

Authors: Douglas J. Seifert, Roland Chemali, Dr. Michael Bittar, Gary Althoff and Amr Lofty

Fig. 1. Producer Well A is placed closer to the roof of the reservoir than producer Well B, resulting in a more efficient sweep and significantly higher ultimate recovery ofthe hydrocarbon in place.


exiting the pay zone during drilling, it is often recommended touse a proactive method2, 3. Geosteering is said to be proactivewhen the approaching boundaries are detected enough inadvance and with enough accuracy to enable a timely change ofcourse to avoid crossing the boundaries. In general, proactivegeosteering is performed using logging while drilling (LWD)sensors capable of estimating the distance between the well andan approaching boundary and identifying the relative azimuthsof that boundary. While sonic and seismic sensors could mapgeological events away from the well path, proactive geosteeringhas almost exclusively relied on electromagnetic sensors.

PROACTIVE GEOSTEERING WITH AZIMUTHAL DEEPRESISITIVTY

For many years, proactive geosteering was performed withtraditional non-azimuthal propagation resistivity LWDsensors4. The resistivity measurements from the sensor arrayexhibit so-called polarization horns when the well approachesa geological boundary. The observed signatures are due to thecontrast in resistivity between the formation being drilled andthe adjacent formation. The horns start appearing as the wellapproaches the boundary, providing a proximity alarm.Because the sensors are non-azimuthal, the real time logs yieldno indication as to the direction of approach. This criticalinformation is then filled in by the geosteering expert, whoseinterpretation is based on inferences and on local knowledge.This guesswork does carry a certain risk, depending on thedepth of the local knowledge and the level of expertise of thegeosteering professional. Figure 2 illustrates the potentialambiguity in interpreting polarization horns from non-azimuthal resistivity LWD sensors.

When the well being drilled in a high resistivity reservoirapproaches a lower resistivity formation, a polarization horndevelops, exhibiting a log reading that exceeds the actualresistivity of the reservoir. The polarization horn is the samewhether the well is approaching the less resistive formationfrom above, from below or even from the side.

The ambiguity of Fig. 2 is dispelled if the deep resistivityLWD sensor is azimuthally sensitive. For a completedescription of azimuthal deep resistivity LWD technology, thereader may refer to Bittar et al., 20082. For the sensor that isthe subject of this article, azimuthal sensitivity is achieved bytilting the receiver antennas. The modeled response of anazimuthal deep resistivity sensor to well paths similar to thosein Fig. 2 is shown in Fig. 3. The first observation is theelimination of the ambiguity of the direction of approach ofthe nearby boundary. Perhaps just as importantly, thepolarization horn on the azimuthal resistivity curve pointingto the reservoir is significantly larger than the non-azimuthalcurve in Fig. 2, indicating a much greater sensitivity toboundaries and therefore a better suitability for geosteering. Inthe example of a well closing in on a conductive roof, Fig. 3right, the “Down” resistivity curve reads 70 ohm-m, seventimes higher than the resistivity in the reservoir.

For a more complex geological environment, whereapproaching boundaries may not be only “above” or“below,” but also “sideways,” it is customary to display theresistivity curves at multiple sectors. There are 32 sectorsaround the circumference of the wellbore. A preferredmethod is to display all 32 sectors as a deep electricalimage5. A polarization horn in that instance appears as abright spot.

One important class of measurements for geosteeringproduced by the azimuthal deep resistivity LWD sensor arrayis the set of so-called geosignals. They were originallydescribed by Bittar 20026. Simply stated, the geosignal for agiven transmitter-receiver pair is obtained by taking thedifference between phase or attenuation readings on opposingsides of the borehole, Fig. 4. The objective of this design is tomaximize sensitivity to lateral changes in resistivities that are


Fig. 2. A polarization horn is observed on a wave resistivity when the wellapproaches the boundary. Because the sensor is non-azimuthal, the same signatureis observed for a well approaching an OWC (left diagram) and for a wellapproaching a low resistivity roof (right diagram).

Fig. 3. Given the same situations as in Fig. 2, the azimuthal deep resistivity sensorexhibits opposing signatures for a well approaching the OWC (left diagram), andfor a well approaching a low resistivity roof (right diagram). In addition, thepolarization horn is significantly more pronounced than that achieved with thenon-azimuthal resistivity sensor, indicating a stronger sensitivity to approachingboundaries.



the sensor array is within the reservoir and the case where it isin the cap rock, seeking the reservoir. The “visibility” from thelow resistivity shale is only 6 ft, but the visibility from thereservoir can reach 20 ft under good conditions. From apractical standpoint, when drilling through the reservoir, thegeosteering expert has an early indication of an approachingboundary. The geosignal enables him/her to alter the well pathto avoid intersecting it.

FACTORS AFFECTING THE MAGNITUDE ANDORIENTATION OF THE GEOSIGNAL

Geosteering in a given geology is more dependable when theresistivity and the geosignal measurements are of high qualityand when they lend themselves to accurate inversion in termsof the distance to the boundary. Simple intuition suggests thata small resistivity contrast between the reservoir and theadjacent formation is likely to generate a weak geosignal. Thisstatement will be supported and quantified later through exactmodeling. Also, a gradational resistivity profile between thereservoir and the adjacent formation is likely to significantlyalter the geosignal. Finally, considering the presence of twoboundaries, one above and one below the well path, it isimportant to recognize their individual as well as theircombined effects on the array response.

Modeling results are given later, along with descriptivecomments to better help understand the key parameters forsuccessful geosteering.

The first series of models gives a qualitative view of thegeosignal in some common situations. Starting with thephysical configurations depicted in Fig. 2 and Fig. 3, computermodeling shows how the geosignal anticipates reservoir exitsand points to the direction of the impending exit, Fig. 6. Thegeosignal is recorded azimuthally and binned in 32 sectors.For ease of understanding, only bin (0), namely, the oneoriented upward, is displayed. In this instance, the reservoircreated by a side-approaching geological event. In a

homogeneous isotropic reservoir with no nearby boundary,the geosignal is clearly zero. When the sensor arrayapproaches a boundary, the geosignal increases steadilyreaching a peak near the point where the well straddles theboundary, Fig. 5. When multiple boundaries are present, thecomputation of the geosignal is more complex. As a firstapproximation, and provided the boundaries are far enoughapart, the resultant geosignal is equal to the sum of theindividual signals created by all the boundaries.

The geosignal is a key geosteering parameter when trying toanticipate and avoid reservoir exits. One general model for“average” geosteering conditions is shown in Fig. 5. Themodel will be expanded in subsequent sections to betterunderstand the factors influencing the response of theazimuthal deep resistivity array.

In the computer simulation of Fig. 5, the magnitude of thegeosignal is compared to the threshold of detection by theelectronics of the sensor. A cursory review of the charthighlights the dissimilarity in response between the case where

Fig. 4. Conceptual description of the geosignal from a transmitter-receiver pair: Asthe sensor array rotates, the phase and attenuation measurements are binned inazimuthal sectors around the circumference. The geosignal is derived bysubtracting signals from opposing bins. In a homogeneous isotropic medium farfrom the boundary, the geosignal is zero. It increases as the well closes in on theboundary.

Fig. 6. Geosignal curves for progressively increasing transmitter-receiver spacingsare displayed for the case of a well nearing a lower resistivity interval. In general,each geosignal points to the less resistive formation, and the longer spacing detectsthe boundary earlier.

Fig. 5. The attenuation geosignal for the 112” spacing is modeled for averageresistivity levels in the reservoir and a 1 ohm-m cap rock. It can be readily seenthat from within the reservoir the upper boundary is detected between 11 ft and20 ft away, depending on the actual resistivity level.


resistivity is assumed to be 10 ohm-m, significantly higherthan the shoulder bed resistivity of 1 ohm-m.

In the case of an impending exit through an OWC belowthe reservoir, the geosignal points downward. Longer spacinggenerally detects the boundary earlier than shorter spacing.Similarly, if the well gets near an overlaying formation of lowerresistivity, the geosignal points upward. The latter is true onlyif the overlaying formation is of lower resistivity than thereservoir. The general rule is that in most cases, geosignalspoint from the higher resistivity interval to the lower resistivityinterval. Sometimes, reversals in this trend are observed due toskin effect. These reversals are beyond the scope of this article,but they are an integral part of the forward and inversionmodeling software associated with the sensor array.

Figure 7 shows the next model used for illustrating theresponse of the geosignal to geological events. A well is drilledupward at a 92° angle in a 2 ohm-m formation. The wellintersects a 10 ft bed of a higher resistivity of 20 ohm-m. Asthe well first approaches the bed boundary, the geosignalpoints downward because the approaching bed resistivity ishigher than the resistivity of the formation. As the wellintersects the first boundary, a characteristic horn appears onthe geosignal curves. Here again, it is observed that the curvefrom the longer spacing detects the event long before the curvefrom the shorter spacing.

The polarity of the geosignal curves honors the rule ofgeosignals pointing from the more resistive bed to the lessresistive bed. This is readily observed in the vicinity of points“A” and “C” in Fig. 7. Note that the longer spacing exhibits asmoother response than the shorter spacing. One additional areaof interest is near the middle of the bed, at point “C.” For the16” curve, the influences of both upper and lower boundariesare negligible, so the geosignal is zero, but for the long spacingof 112”, both boundaries are still within detection range. Some -where near the middle of the bed their combined effects nulleach other, yielding a steep rate of change, around a zero value.

The geosignal curves represented in Fig. 7 correspond tothe two ends of the range of available measurements. The 16”phase at 2 megahertz (MHz) being the shallowest investi -gation and the 112” phase at 500 kilohertz (kHz) beingamong the deepest looking. Depending on the actual resistivityvalues and contrasts, and the bed thicknesses surrounding thewell, lower frequency measurements from the 112” spacingmay investigate even deeper. The attenuation geosignal fromthe 112” at 125 kHz sub-array is sometimes used in very lowresistivity environments.

FACTORS AFFECTING THE ROBUSTNESS OF THEINTERPRETATION OF THE GEOSIGNAL

The modeling examples reported in the previous sectionwould suggest a simple and predictable response of thegeosignal to geological boundaries. In reality, most of thereservoirs being geosteered exhibit complex stratigraphy, withmore extreme resistivity levels and variability than previously

shown. In this short study, various parameters are consideredto evaluate what makes a reservoir easy to geosteer into, andwhat the more challenging situations are.

The first surprising observation about the geosignalmagnitude comes from a cursory review of Fig. 5. The actualresistivity ratio between the reservoir and the adjacentformation is almost irrelevant once it has exceeded the valueof 10:1. If the geosteering expert were to take the curvecorresponding to “Rt = 30 ohm-m” as a generalized responsefunction, the maximum error on the distance to the bedboundary would stay within 20% of the actual value. Thisprecision would apply from a resistivity ratio of 10:1 to aresistivity ratio of 1,000:1. What seems to be more relevant isthe resistivity of the approaching boundary.

One additional observation from Fig. 5 and from Fig. 7 isthe inability of the deeply spaced geosignal to resolve theboundary with any more accuracy than 2 ft. In practicalterms, this observation confirms that when the sensor isstraddling the boundary the deepest reading sub-array is notcapable of recognizing whether or not the well has actuallyexited the reservoir. The geosteering expert then relies on theshallower reading arrays, including the 16” 2 MHz phase, oron a borehole imaging sensor to remove this uncertainty.

The resistivity contrast that appears to be most critical forproviding a robust, high amplitude geosignal is not themathematical ratio of resistivities; it is the difference inconductivities between the reservoir and the adjacent bed.This observation holds true for the case where the reservoir ismore resistive than the adjacent formation; Fig. 8 supportsthis assertion. Starting with the geological situation simulatedin Fig. 5 the ratio Rt/Rs is left fixed at 5, but the resistivitiesof both the reservoir and the overlaying shale are allowed to


Fig. 7. Modeling of geosignals when the well intersects a 10 ft bed.



gradational boundary. The example in Fig. 10 shows how agradational boundary alters the geosignal from a similarsituation, but with a sharp boundary. Inversion algorithms arenot yet capable of taking such events into account, making itdifficult to recognize the boundary with accuracy. Themeasurements may still be used to provide a direction to theboundary and an approximate assessment of the distance tothe bed boundary.

PRACTICAL EXAMPLES OF NAVIGATING INCHALLENGING RESERVOIRS

In the first example, the target was known to be a thin layerof hydrocarbon bearing sandstone. The challenge was to landthe well and rapidly build the angle to avoid exiting throughthe floor, then follow the reservoir for several hundred feet.The example in Fig. 11 illustrates how well the geosignalsfrom two different spacings helped to recognize the reservoirroof, the reservoir floor, and then the reservoir mid-pointbefore finally exiting through the roof.

As seen by analyzing the elements of Fig. 11 from left toright, the geosignals show the roof immediately above the wellas it enters the reservoir. The curves then come together nearzero (point A) indicating that the well is near the middle of thebed. This type of response was previously illustrated in Fig. 7.The action by the driller at that stage is to rapidly build the

vary. While the ratio of resistivity values remains constant, thedifference in conductivity varies in inverse proportion to theshale resistivity. The maximum signal near the boundaryincreases monotonically with the decrease of the shaleresistivity. Away from the boundary, the signal drops fasterdue to the skin effect as observed earlier.

A further illustration is provided in the example of Fig. 9,where the shale cap rock of 10 ohm-m is replaced by ananhydritic formation of 1,000 ohm-m resistivity. The geosignalin this case is reversed, but its magnitude is approximately 10times smaller, although the 10:1 contrast is the same in bothinstances. To preserve sensitivity at these high resistivity levelsthe geosignal represented in Fig. 9 was selected to be the 48”phase at 500 kHz.

When geosteering below an anhydrite formation, it isessential to keep in mind that the geosignal is reversed indirection from the more common case of a conductive caprock. It is also important to account for the fact that thesignal magnitude is significantly smaller.

The final modeling aspect of this study concerns the case of

Fig. 8. The resistivity contrast between the reservoir and the adjacent shale plays adeterminant role in the strength of the geosignal. The relevant type of contrast isthe difference in conductivity between the reservoir and the adjacent bed ratherthan the ratio of resistivities.

Fig. 11. Geosteering with images and with geosignals: The challenge is to staywithin a thin bed portion of the reservoir. The landing appears clearly on the densityimage. Points A, B and C correspond to the well coming through the middle of thebed, coming close to the floor, then building back up toward the roof.

Fig. 10. A gradational boundary alters the geosignal sometimes to the point ofmaking inversion algorithms highly imprecise.

Fig. 9. This example further supports that the relevant contrast is the difference inconductivity between the adjacent formation and the reservoir. In the two casesdescribed in this model, the resistivity ratio is the same, but the difference inconductivity is 10 times larger in the case of the shale cap rock than in theanhydrite cap rock.


angle since the floor is presumably not too far down. At pointB, one may suspect an exit through the floor, but the densityimage shows that the well has stayed in the porous portion. Asthe driller builds the angle, the well finds itself again near themiddle of the reservoir, at point C. To the right of point C,there is evidence that the well exits through the roof.

The second example, Fig. 12, uses the separation of the“up” and “down” resistivity curves to recognize a proximityto the roof of the reservoir.

In this example, the separation between up resistivity anddown resistivity is similar to the observed patterns of Fig. 3.Because not all geological events are horizontal, it issometimes important to transmit to the surface a real timeimage of all azimuthal bins. The separation indicates that thewell path came very close to the reservoir roof or to a nearhorizontal conductive layer on the right-hand side of thefigure. With this form of display, a large separation betweenup and down resistivity curves due to a polarization hornoften translates into a bright spot on the image5.

CONCLUSIONS

A series of computer models employing both simple andrelatively complex well paths and geological settings wasreported. The models have helped drillers to better understandthe response of the azimuthal deep resistivity sensor array andhow it can be used for proactive geosteering in complexgeologies.

Of the many measurements provided by the sensor, thedeep azimuthal resistivity images, in particular the “up-down”resistivity logs, recognize when a boundary is near the wellpath. Longer spacing measurements reach farther in theformation and recognize approaching boundaries earlier. Themodeling effort also focused on the geosignal and how it canbe used to quantify the distance to boundaries. It was shownthat geosteering with the geosignal is most effective when thereservoir exhibits high resistivity, but most importantly whenthe approaching undesired geological event has low resistivity.

The strength and robustness of the geosignal are best whenthe difference in conductivity between the reservoir and the

adjacent formation is high. This occurrence is common nearan OWC or the reservoir-shale interface. It was also shownthat when the cap rock is highly resistive, such as anhydrite,proactive geosteering becomes much more challenging becausethe geosignal is reversed from the normal orientation, nowpointing away from the roof, and also because it issignificantly weaker since the difference in conductivity ismuch reduced. Finally, a case where a gradational resistivitychange occurs at a boundary was modeled and shown tocreate artifacts on the geosignal, making it more challengingto geosteer.

ACKNOWLEDGMENTS

The authors wish to thank the management of Saudi Aramcoand Halliburton for supporting this study and for releasingthe data for publication.

This article was prepared for presentation at the SPE/IADCDrilling Conference and Exhibition, Amsterdam, theNetherlands, March 1-3, 2011.

REFERENCES

1. Bell, C., Hampson, J., Eadsforth, P., et al.: “Navigating andImaging in Complex Geology with Azimuthal PropagationResistivity While Drilling,” SPE paper 102637, presentedat the SPE Annual Technical Conference, San Antonio,Texas, September 24-27, 2006.

2. Bittar, M., Hveding, F., Clegg, N., Johnston, J., Solberg, P.and Mangeroy, G.: “Maximizing Reservoir Contact in theOseberg Field Using a New Azimuthal Deep-ReadingTechnology,” SPE paper 116071, presented at the SPEAnnual Technical Conference and Exhibition, Denver,Colorado, September 21-24, 2008.

3. Seifert, D., Al-Dossari, S., Chemali, R., et al.: “DeepElectrical Images, Inversion and Real Time Inversion HelpGuide Steering Decisions,” SPE paper 123940, presented atthe SPE Annual Technical Conference and Exhibition, NewOrleans, Louisiana, October 4-7, 2009.

4. Seydoux, J., Tabanou, J., De Leat, Y., et al.: “A DeepResistivity Logging While Drilling Device for ProactiveGeosteering,” The Leading Edge, Vol. 23, No. 6, June2004, pp. 581-586.

5. Chemali, R., Bittar, M., Hveding, F., Wu, M. and Dautel,M.: “Integrating Images from Multiple Depths ofInvestigation and Quantitative Signal Inversion in RealTime for Accurate Well Placement,” SPE paper IPTC12547, presented at the International PetroleumTechnology Conference, Kuala Lumpur, Malaysia,December 3-5, 2008.

6. Bittar, M.: “Electromagnetic Wave Resistivity Tool Havinga Tilted Antenna for Geosteering within a Desired PayZone,” U.S. Patent 6,476,609, November 5, 2002.


Fig. 12. Geosteering with images and with up-down resistivity curves. The deepresistivity image helps verify that all events in this section are either above orbelow the well path; no side boundary is seen.



Dr. Michael Bittar is Director ofFormation Evaluation Technology forHalliburton Drilling and Evaluation.He joined Halliburton through Sperry-Sun Drilling Services in 1990. Michaelhas contributed to the advancement oflogging while drilling (LWD), wireline

resistivity and logging sensor technology and interpre tation,most recently as the Sensor Physics Manager for SperryDrilling Services and Wireline and Perforating Services. Heled the team that developed the InSite ADR sensor.

Michael received his B.S., M.S. and Ph.D. degrees, all inElectrical Engineering, from the University of Houston,Houston, TX, in 1983, 1986 and 1990, respectively. He hasmore than 20 patents and is the author of more than 20publications.

In 2006, Michael received the Society of Petrophysicistsand Well Log Analysts (SPWLA) Technical AchievementAward.

Gary Althoff is the Sperry DrillingReservoir Solutions TechnicalApplication Manager. He has served inmultiple operational, program manage -ment and technology roles since joiningSperry Drilling in 1984. Gary has beenintimately involved in the develop ment

and deployment of numerous logging while drilling (LWD)services, including: Directional Gamma, Bimodal AcousticTool, and Magnetic Resonance Logging While Drilling, tocite a few. More recently, he successfully managed theAzimuthal Deep Resistivity project.

In 1984, Gary received his B.S. degree in PetroleumEngineering from the University of Missouri, Rolla, MO.

Amr Lotfy is an Account Manager forHalliburton in Egypt and waspreviously the Petrophysics andGeosteering Lead for Halliburton-Sperry Drilling Services in SaudiArabia.

He received his B.S. degree inPhysics from the American University in Cairo, Egypt. Amris now completing his Masters degree in BusinessAdministration.

He has coauthored multiple articles in national andinternational publications. Amr is an active member of boththe Society of Petroleum Engineers (SPE) and the Society ofPetrophysicists and Well Log Analysts (SPWLA).

BIOGRAPHIES

Douglas J. Seifert is a PetroleumEngineering Specialist who specializesin logging while drilling (LWD) loginterpretation and applications,geosteering and logging operations.Before joining Saudi Aramco in 2001,he was the Western Hemisphere

Regional Petrophysicist for Pathfinder Energy Services inHouston, TX, and the Eastern Hemisphere RegionalPetrophysicist in Stavanger, Norway. Doug also worked asthe Senior Petrophysicist for Mærsk Olie og Gas inDenmark; for Halliburton Energy Services in variousoperational, research and technical support functions, andfor Texaco in their Technical Services and ProductionOperations.

Doug is currently President of the Saudi PetrophysicalSociety, the Saudi Arabian Chapter of the Society ofPetrophysicists and Well Log Analysts (SPWLA). He alsoserved on the SPWLA Technology Committee (1985-1988)and as President and in various other officer positions forthe Houston Chapter of the SPWLA (1986-1990).

He received a B.S. degree in Statistics and a M.S. degreein Geology, both from the University of Akron, Akron, OH.

Roland Chemali is Chief Petrophysicistfor Halliburton-Sperry DrillingServices. He has coauthored multiplepapers and patents on formationevaluation, including geosteering highangle wells, magnetic resonance andformation pressure measurement while

drilling. In 1997, Roland received the TechnicalAchievement Award from the Society of Petrophysicists andWell Log Analysts (SPWLA), and in 1988 he received theTechnology Innovation Award from Petroleum EngineerInternational. Roland was also the Distinguished Memberof the Technical Staff at Halliburton in 1991 and theSPWLA Distinguished Speaker in 2005 and in 2008.

He received an Engineering degree in 1966 from theEcole Polytechnique of Paris, France and a PetroleumEngineering degree in 1967 from the Institute Francais duPétrole (IFP), Rueil-Malmaison, France. Roland alsoreceived his M.S. degree in Mathematics in 1969 fromLouisiana State University, Baton Rouge, LA.


ABSTRACT

Drilling horizontal wells has become more commonplace inmany Saudi Aramco fields. Recent advances in drilling andcompletion practices have enabled the drilling of morecomplex wells of various types and shapes — such as extendedreach wells, bilaterals and multilaterals — for providingmaximum reservoir contact (MRC). These complex wells posegreat challenges with respect to rigless well interventions. Oneof the initial well interventions, which follow drillingcompletion, is acid stimulation to remove formation damage.The challenges raised by well complexity include accessing thelaterals, reaching the end of each lateral and properlydistributing treatment fluids over entire horizontal sections.

Effective rigless interventions in these challenging wells havelimitations and technologies are still evolving. Deployment oftools and fluids downhole is now made more possible throughconventional coiled tubing (CT) applications. New techniqueshave been developed to assist CT to access laterals and extendtheir reach. In addition, recent advance ments in the CTbottom-hole assembly (BHA) allows combining the services ofmore than one function — such as deploying tools and fluids— while at the same time acquiring vital downhole data.

The fiber optic enabled CT (FOECT) provides thecombined services of data acquiring in real time and runningcrucial CT technology tools. By virtue of the optical fiber,distributed measurements can be made available to obtaintemperature profiles. The temperature surveys allow theevaluating of flow distribution through temperature responses,therefore, optimizing acid stimulation. The FOECT, with aflow-through BHA, coupled with a smart combination oftechnology tools — such as a multilateral tool (MLT) and amechanical drag reducer tool — provides integrated services.

This article highlights the first ever worldwide combinationof technology tools via FOECT for acquiring downhole datain real time, and technology tools for accessing and extendingthe reach of the CT for stimulating a trilateral oil producer ina major carbonate reservoir in Saudi Arabia. Thiscombination resulted in the successful acid stimulation of allthree laterals. The article also discusses in detail theapplication of distributed temperature sensors for placingtreatment fluids and lessons learned.

BACKGROUND

Recent advances in drilling and completion practices haveenabled the drilling of more complex wells of various typesand shapes, such as extended reach wells, bilaterals,multilaterals, and maximum reservoir contact (MRC) wells,etc. The benefits of drilling these wells include reduction indevelopment and operating costs, and an improvement inreservoir performance and management1, 2.

Well A was drilled and completed as a trilateral open holeoil producer. Figure 1 shows the trajectories of the laterals.The well was cased with 95⁄8” casing to the top of theproducing reservoir. An 8½” hole was then horizontallydrilled 3,000 ft inside the reservoir. A 7” liner was run andcemented. The motherbore (L-0) was next drilled successfullyfor a total horizontal footage of about 3,000 ft. After that, thefirst lateral (L-1), about 3,000 ft, was drilled after opening acasing window about 1,200 ft above the liner shoe. Followingthat, a second lateral (L-2), about 3,000 ft, was drilled from acasing window 2,800 ft above the liner shoe. The well wascompleted with tubing and equipped with an electricalsubmersible pump (ESP) to produce the well. The ESPassembly has a bypass (Y-tool), which allows normal wellinterventions and other well surveys. The minimum insidediameter (ID) is 2.441” downhole in the tailpipe3.


Smart Combination of Technology ToolsResulted in a Successful Rigless Stimulationon a Trilateral Well: Case Study

Authors: Ahmed K. Al-Zain, Abdulwafi A. Al-Gamber and Rifat Said

Fig. 1. Lateral trajectories of Well A.



first run, then L-1 in the second run, and finally L-2 in thethird run. Each lateral was divided into sections, and DTSprofiles were recorded at the top of each section. The DTSprofiles were used to properly place the treatment fluids, boththe acid diverter and the plain acid.

In the first run, the FOECT was run in the motherbore leg.Access to the motherbore was confirmed immediately and inreal time upon entry by the casing collar locator (CCL) log.Figure 4 shows the CCL signals at the casing shoe. The

After completion, an attempt was made to flow the well forcleanup via the ESP, but it was unable to flow, indicating severeformation damage. The main reason for this drilling damage wasthat drilling the well overbalanced had exposed the formationface to drilling fluids. By design, the drilling fluid forms a filtercake at the sand face to avoid mud losses and prevent filtratesfrom going deep into the formation. Mud cake and filtrates werethe main causes for the Well A drilling damage. Acid stimulationwas required to remove the formation damage4, 5.

INTEGRATED JOB PROGRAM

An integrated program was devised and executed successfullyfor the first time in the subject trilateral horizontal well. Theprogram called for utilization of fiber optic enabled coiledtubing (FOECT), which provides the combined services ofacquiring data in real time and running crucial coiled tubing(CT) technology tools. The FOECT with a flow-throughbottom-hole assembly (BHA), coupled with a smartcombination of technology tools, such as a multilateral tool(MLT) to enter the laterals and a mechanical drag reducer toolto extend the reach of the CT, provided the integrated services3.

Due to the inside diameter (ID) restriction of the tailpipe, aCT tractor was not an option to extend the CT reach toachieve an even distribution of the treatment fluids along theentire horizontal sections. Instead, the extended reach in eachlateral was achieved by using a combination of mechanicaland chemical friction reducers. This combination was basedon experience gained in stimulating extended reach wells. Themechanical action was achieved using an agitator tool6.

Another objective of using the FOECT was to conduct adistributed temperature sensor (DTS) survey via the fiber opticcable while stimulating each lateral. The distributed temp eratureswere measured through fiber optic cable from the top of the wellto the target depth by sending bursts of light down the fiber opticcable. The temperature measurements enabled an evaluation ofthe flow distribution through temperature responses. Distributedtemperature measurements were used to obtain temperatureprofiles. Interpretations of those DTS profiles during the courseof the job execution helped to enhance the procedure to ensureproper placement of the treatment fluids; therefore, optimizingacid stimulation. The DTS survey on this first job has since beenused as a base in evaluating current practices — in acidstimulating horizontal, extended reach and MRC wells — for thepurpose of stimulation optimization7.

Figure 2 shows the fiber optic cable in its carrier in a CT.Figure 3 illustrates the BHA used during the main acid job. Abrief system description of the FOECT and BHA, the design ofthe treatment fluids, function tests of the CT BHA and job oper -ations are given more elaboration by Al-Zain et al., 20093, 8.

JOB EXECUTION AND ACCESSIBILITY OF LATERALS

Three runs with the FOECT were made to treat all laterals.Acid treatments were performed for the motherbore in the

Fig. 2. Fiber optic cable in its carrier inside a CT.

Fig. 3. Drawing of CT BHA used during the job.


absence of a CCL signal below the casing shoe indicates entryinto the open hole of the motherbore. The use of a CCL fordepth correlation and lateral entry confirmation is a superiormethod compared to previous impractical methods thatrequired running the CT to the total depth (TD).

At the casing shoe, slugs of chemical friction reducer werepumped and the agitator was activated. To avoid CT lockup,pumping of the preflush stage was begun and continued untilthe TD was reached. At the TD, temperature profiles were

made via the DTS. Based on open hole logs and assisted bythe DTS profiles, the motherbore was divided into threesections – toe, middle and heel – which were stimulated byalternating between acid and diverter. The FOECT wasstationed to conduct temperature profiles at the top of eachsection. These DTS profiles were used to evaluate theperformance of the treatment fluids — pumped across thepreceding section — and to aid in placing treatment fluids forthe subsequent section.

In the second run, the FOECT accessed L-1 using the MLT.Once the FOECT neared the casing window, the MLT wasactivated to access the lateral. Three up and down passes weremade with the FOECT across the window. During the thirdattempt in less than one hour, a positive indication that theFOECT had accessed the window was received. Figure 5depicts these attempts. The access was confirmed by theabsence of a CCL signal, Fig. 6, upon entry into the openhole. The FOECT, assisted by the agitator and chemicalfriction reducer, was able to reach to the TD.

Similarly, in the third run, L-2 was accessed by the FOECTusing the MLT; the agitator tool was not needed for this runand removed from the BHA. Nine up and down passes wereattempted across the casing window. The window wassuccessfully located during the tenth attempt and the lateralwas accessed. The reentry was confirmed by the differentialpressure and the CCL. The CT successfully reached to the TD.

APPLICATIONS OF DISTRIBUTED TEMPERATURESENSING FOR PROFILING

The DTS profiles were used to place the treatment fluidsbefore, during and after each stage. This placement helped toevaluate the performance of the treatment fluids pumpedacross the preceding section and to aid in placing treatment


Fig. 4. CCL log showing a clear signal of the liner shoe without a signal below theshoe, indicating entry into the open hole.

Fig. 5. Activation of the MLT to enter L-1, with a positive indication after thethird attempt.

Fig. 6. CCL log showing a clear signal of the L-1 window and no signal below thewindow, indicating entry into the open hole.



the acid and the carbonate formation. This meant thatalthough the treatment fluids were pumped into thebottom 800 ft toe section, acid had traveled through thebackside of the CT and penetrated the good porosityopen hole matrix up to 10,100 ft. The lack of appre -ciable heating below 10,100 ft coincides with a changein the rock quality at about 10,100 ft, as shown in theopen hole log at the top of Fig. 9. Based on thisobservation, it was decided to pump the second acidstage both over the middle section and over the poroussection between 10,100 ft and 11,800 ft.

• In addition to the accelerated heating, the temperatureprofile over the middle section shows distinct local hotspots, indicating zones that had taken more acid. Thisobservation was beneficial to target the hot spots withan acid diverter.

fluids for the subsequent section. For the purpose of thisarticle, discussion of temperature profiles is limited to thoseprofiles made for the motherbore.

Preflush Stage

At the casing shoe, pumping of the preflush stage was begunand continued until 12,660 ft, or 380 ft from the TD. Slugs ofchemical friction reducer were pumped and the agitator wasactivated to avoid CT lockup. Distributed temperatures wererecorded via the DTS with the FOECT stationed at about12,600 ft. Figure 7 depicts the temperature profile recordedafter pumping of the preflush was completed.

Based on the temperature profile obtained after pumping ofthe preflush, it was decided to pump the first acid stage totreat an 800 ft interval at the toe section, between 12,600 ftand 11,800 ft.

First Acid Stage

The toe section was acid washed while pulling out of hole(POOH) with the FOECT. Slugs of acid diverter were pumpedacross three intervals and exhibited slow warming, recordedin the initial preflush temperature profile, Fig. 7. After acidwashing to 11,800 ft, the FOECT was stationed to conduct atemperature profile to evaluate temperature responses afterpumping the first stage across the toe section; this would aidin placing treatment fluids for the following section. Figure 8shows the temperature profile in red, obtained by the DTSfollowing the first acid stage. Examination of the temperatureprofile shows two distinctive portions. The first portion at theheel is almost at the same preflush temperature gradient,illustrated with a dashed blue line. The second portion,between 10,100 ft and 11,800 ft, deviates sharply from thedashed blue line, indicating heating effects. Local hot spots arealso evident. Two main inferences can be made from thisprofile:

• Heating clearly occurred at 10,100 ft and beyond (overthe middle section of the open hole). This acceleratedheating resulted from an exothermic reaction between

Fig. 7. Temperature profile for the motherbore after completing the preflush.

Fig. 8. Temperature profile (red) after completing the first acid stage.

Fig. 9. Normalized temperature profiles recorded via the DTS while treating themotherbore.


Second Acid Stage

Pumping was then resumed to acid treat the middle section,from 11,800 ft to 10,100 ft. The treatment fluids werepumped while POOH with the FOECT. Slugs of acid diverterwere also pumped across the hot spot intervals observed fromthe temperature profile, as previously discussed. At 10,100 ft,the FOECT was stationed to obtain a DTS temperatureprofile. Figure 10 shows the temperature profile (in green)obtained by the DTS following the treatment of the middlesection. The temperature profile over the heel did not indicatea major change from the preflush thermal gradient (dashedblue line) except for the development of two hot spots at9,500 ft and 9,800 ft. This observation indicated that most ofthe treatment fluids had flowed to the good rock middlesection, not to the poor rock heel section, which exhibited lowacid heating. Unfortunately, running in hole (RIH) with CT toevaluate the thermal responses in the middle section — afterpumping the second stage — was not possible due to amalfunction in the BHA.

Third Acid Stage

The heel section was then acid treated while POOH with theFOECT. Slugs of acid diverter were pumped across the twohot spots observed, as previously discussed. By this stage, thestimulation of the motherbore was completed and the CT wasPOOH to inspect and replace the BHA in preparation fortreating the second lateral.

Normalized DTS Temperature Profiles

As previously discussed, Fig. 9 shows temperature profiles(after normalizing) — recorded via the DTS — while treatingthe motherbore. Normalizing was achieved by applyingcorrection factors so that all the profiles met the base preflushprofile at the liner shoe, immediate to the open hole.Normalizing temperature data eliminates the effects oftemperature variation in the injected fluids and removes thevariations of warm-back or cool-down over time. Also,normalizing enables the identification of heating due to acidreaction (exothermic) and formation temperature.

Four profiles are provided in Fig. 9. The first profile in lightblue was recorded right after reaching the TD and completingthe pumping of the preflush stage. The second profile in darkblue was conducted three hours after the first profile. Thermalrecovery was evident, as seen by a warming back toward thegeothermal gradient.

The third profile in red was taken with the FOECTstationed at 11,800 ft, after acid treating the bottom 800 fttoe section. The last profile in green was made aftercompleting treatment of the middle section with the FOECTstationed at 10,100 ft.

It can be seen from the red profile that there was asignificant temperature increase as compared to that of thepreflush profiles. This accelerated heating trend was in

response to the exothermic reaction of the acid and thecarbonate formation. The red profile also indicates thatalthough the treatment fluids were pumped in the bottom 800ft section, they traveled through the backside of the CT andpenetrated the good open hole matrix up to 10,100 ft. Thisexplanation coincides with a change in the quality of rock at10,050 ft, as shown from the open hole log in the top of theplot.

In addition to the accelerated heating trend, the post-acidDTS profiles show distinct local hot spots, indicating zonesthat had taken more acid. Slugs of acid diverter were placedwhile treating the middle section to target the hot spots thathad developed after treating the toe section, as seen from thered profile. Unlike the red profile, the green temperatureprofile indicates low acid heating in the heel, corresponding toa poor rock quality interval and suggesting flow to the middleporous section.

Acid Treatments of L-1 and L-2

In a similar manner, L-1 and L-2 were divided into sectionsand stimulated. DTS profiles after stimulating each sectionwere also recorded. The profiles helped in identifying the hotspots, and consequently, in placing the treatment fluids.Diverter slugs were pumped while tripping out each section,and then the acid was pumped — while tripping in the subjectsection — whenever RIH was possible without the need toreactivate the agitator tool.

CONCLUSIONS

1. Combining technology tools resulted in a successfulintervention for the subject trilateral oil well. Futuredevelopment of other tools, such as for gamma ray,inclination, azimuth, etc., would expand the FOECTutilization to more complex well geometries, such as openhole multilaterals.


Fig. 10. Temperature profile (green) after completing the second acid stage.



2. Saleri, N., Salamy, S., Mubarak, H., Sadler, R., Dossary, A.and Muraikhi, A.: “Shaybah-220: A Maximum ReservoirContact (MRC) Well and Its Implications for DevelopingTight-Facies Reservoirs,” SPE paper 88986, SPE ReservoirEvaluation & Engineering, August 2004, pp. 316-321.

3. Al-Zain, A.K., Said, R., Gamber, S., Al-Driweesh, S., Al-Shahrani, K. and Jacobsen, J.: “First WorldwideApplication of Fiber Optic Enabled Coiled Tubing withMultilateral Tool for Accessing and Stimulating a TrilateralOil Producer, Saudi Arabia,” SPE paper 126730, presentedat the Kuwait International Petroleum Conference andExhibition, Kuwait City, Kuwait, December 14-16, 2009.

4. Al-Zain, A.K., Al-Driweesh, S., Jandal, A., Said, R.,Metwally, S. and Abitrabi, A.: “An Innovative StimulationApproach for Horizontal Power Injectors in a SandstoneReservoir, Saudi Arabia: Field Experience,” SPE paper119997, presented at the SPE Middle East Oil and GasShow and Conference, Bahrain International ExhibitionCenter, Manama, Bahrain, March 15-18, 2009.

5. Al-Mulhem, N., Sharma, H.K., Al-Zain, A.K., Al-Suwailem, S. and Al-Driweesh, S.: “A Smart Approach inAcid Stimulation Resulted in Successful Reviving ofHorizontal Producers Equipped with ICD Completions,Saudi Arabia: Case History,” SPE paper 12738, preparedfor presentation at the SPE International Symposium andExhibition on Formation Damage Control, Lafayette,Louisiana, February 10-12, 2010.

6. Nasr-El-Din, H., Arnaout, I., Chesson, J. and Cawiezel, K.:“Novel Techniques for Improved CT Access andStimulation in an Extended Reach Well,” SPE paper94044, presented at the SPE/ICoTA Coiled TubingConference and Exhibition, The Woodlands, Texas, April12-13, 2005.

7. Sharma, H.K., Al-Zain, A.K., Al-Salman, N., Al-Harbi, M.and Said, R.: “A Successful Application of PermanentlyInstalled Distributed Temperature Sensing (DTS) forOptimization of Acid Treatment in Power Water Injectorwith Advanced Well Completion: Case Study,” SPE paper132746, prepared for presentation at the SPE AnnualTechnical Conference and Exhibition, Florence, Italy,September 19-22, 2010.

8. Al-Zain, A.K., Duarte, J., Haldar, S., et al.: “SuccessfulUtilization of Fiber Optic Telemetry Enabled Coiled Tubingfor Water Shut-off on a Horizontal Oil Well in GhawarField,” SPE paper 126063, presented at the SPE SaudiArabia Section Technical Symposium and Exhibition, al-Khobar, Saudi Arabia, May 9-11, 2009.

2. The use of a MLT successfully allowed the FOECT toaccess all laterals.

3. Combining the agitator tool with a chemical frictionreducer helped to reach the TDs of all laterals.

4. The FOECT enabled us to obtain reliable and real-timedownhole data, such as CCL, bottom-hole temperature(BHT), and bottom-hole pressure (BHP). This is a realbreakthrough for most CT applications, as it allowsdownhole communication directly and continuously withthe wellbore, reservoir or surface tools.

5. Real time data of BHT, BHP and the CCL confirmed accessto laterals upon entry. The use of a CCL for depthcorrelation and lateral entry confirmation is a superiormethod compared to previous impractical methods thatrequired running the CT to the TD.

6. The DTS profiles recorded using the FOECT were viableoptions enabling us to build an exact picture of thetreatment performance in real time and to act decisivelywith more confidence. The placement of treatment fluidswas based on thermal responses monitored via the DTSduring various job stages.

7. The exothermic effects were clearly observed as acceleratedheating in the post-acid DTS profiles. This exothermiceffect indicated that acid had penetrated the formation andreacted with carbonate rock.

8. The workflow for stimulation treatment using DTS wasestablished, and the lessons learned from the DTS profiles— recorded on this trilateral well during the acid treatment— were evaluated to optimize subsequent acid stimulationjobs for long horizontal and extended reach wells.

ACKNOWLEDGMENTS

The authors would like to thank Saudi Aramco managementfor permission to publish and present this article. The authorswould also like to acknowledge the efforts and contributionsof Jan Jacobsen of Schlumberger for his technical input andsupport.

This article was prepared for presentation at the SPEMiddle East Oil and Gas Show and Conference, Manama,Bahrain, March 20-23, 2011.

REFERENCES

1. Mubarak, S., Pham, T., Shamrani, S. and Shafiq, M.:“Using Downhole Control Valves to Sustain Oil Productionfrom the First Maximum Reservoir Contact, Multilateraland Smart Well in Ghawar Field: Case Study,” SPE paper11630, presented at the SPE Intelligent Energy Conferenceand Exhibition, Amsterdam, the Netherlands, February 25-27, 2008.



BIOGRAPHIES

Ahmed K. Al-Zain currently works as aProduction Engineering Specialist inwell treatments in the Southern AreaProduction Engineering Department(SAPED).

He received his B.S. degree in 1989in Petroleum Engineering from Tulsa

University, Tulsa, OK. After his graduation, Ahmed joinedSaudi Aramco as a Production Engineer and worked invarious sandstone and carbonate major fields in theSouthern area. He now has over 20 years of experience,mainly in production engineering as well as in reservoir anddrilling engineering.

Ahmed has published several technical papers on varioustopics, such as acid stimulation, scale inhibition, watercompatibility, coiled tubing applications and automated welldata acquisitions.

Abdulwafi A. Al-Gamber is currentlyfilling the position of GeneralSupervisor of the ‘UdhailiyahProduction Engineering Division.During his career of more than 22years with Saudi Aramco, Abdulwafihas headed different Well Services and

Production Engineering Divisions, using his experience inthe areas of production, drilling and workover engineering,as well as reservoir management.

In 1988, Abdulwafi received his B.S. degree in PetroleumEngineering from King Fahd University of Petroleum andMinerals (KFUPM), Dhahran, Saudi Arabia.

Rifat Said has more than 20 years ofexperience in the oil and gas industry,specifically in cementing, coiled tubingoperations and stimulation services,including matrix stimulation andfracturing. He worked forSchlumberger for 18 years before

joining Saudi Aramco in September 2006. Currently, Rifatworks as a Stimulation Engineer providing technical supportto the Southern Area Production Engineering Department(SAPED).

He received his B.S. degree in 1986 in MechanicalEngineering from the University of Indonesia, Jakarta,Indonesia.


creo

ABSTRACT

A compact, multiphase Inline Water Separation (IWS) fieldprototype system has been developed and trial tested. Thesystem consists of a Gas-Liquid Cylindrical Cyclone (GLCC),a Liquid-Liquid Pipe Separator (LLPS), a Liquid-LiquidCylindrical Cyclone (LLCC) and a two-stage Liquid-LiquidHydrocyclone (LLHC). The function of the system is toseparate a significant portion of the produced water from aproduction stream while the remaining production fluids ofgas, oil and reduced water are then sent to existing processingfacilities. The quality of the separated water meets therequirements for water injection or direct disposal. Thecompact IWS can be installed either at the wellhead ordownstream of a production manifold to handle multiplewells on land or on offshore platforms. The benefit of such asystem includes increasing field water handling capacity,reducing the size and cost of the downstream separationfacility, and reviving dead low-pressure wells.

The field prototype has been successfully tested in the field.The overall water separation efficiency, defined as thepercentage of water removed from the total water production,averages 70%. The outlet water quality is less than 35 ppmoil concentration. The results of the tests show that it isfeasible to integrate GLCC, LLCC, LLPS and LLHC into acompact multiphase separation system for bulk producedwater removal from a production stream.

INTRODUCTION

As oil fields mature, more and more water is produced withthe production of oil and gas. This is especially true whenwater injection is used to maintain reservoir pressure andincrease oil recovery. As we strive to achieve ever higherrecovery targets using water flooding as a key enabler,produced water handling will become a significant issue dueto its increasing volume.

A common production water handling strategy iscentralized processing. In this scheme, productions fromindividual wells are gathered into manifolds via flow lines andare transported to the gas-oil separation plant (GOSP) vialarge diameter trunk lines. At the GOSP, separation andprocessing of oil, gas and water take place within large

conventional vessels. The processed water is either dischargedat nearby water disposal wells or sent back to the field forwater re-injection into the reservoir. With increasing waterproduction, several complications can develop:

• Water volume can exceed the GOSP’s water handlingcapacity, which can cause processed water to be offspec. The off spec water in turn can lead to a decline inwater injectivity for wells and to the need for regularstimulation treatments. Upgrade or expansion of aGOSP’s water handling capacity with conventionalbulky separation systems can be costly.

• Existing gathering systems can become a bottleneck tooil production. To maintain oil production, the totalfluid produced will increase as the water cut increases,and the gathering system capacity will become a limitingfactor. Laying extra flow lines or trunk lines can beexpensive, especially offshore.

• Wells can cease to produce due to increasing systemback pressure as a result of the increasing water volumebeing transported through the gathering systems.

• Higher water production leads to higher chemical use(demulsifiers and corrosion inhibitors) and, therefore,higher processing costs. In addition, energy consump -tion will increase as large quantities of water arepumped from the field to the GOSP and then from theGOSP back for re-injection into the wells in the field.

A number of methods are used to reduce the quantity ofwater produced to the surface. These methods includerestricted production, cyclic production, mechanical orchemical water shut-off, and short radius horizontal drilling.For new wells, the use of production equalizers and smartcompletions can be broadly viewed as ways to also controlwater production. The next best place to handle waterproduction is in the wellbore, however, downhole oil-waterseparation and re-injection still remains a challenge even after20 years of industry research and development.

More and more water will have to be produced to thesurface as oil fields mature, and as artificial lift and enhancedoil recovery (EOR) methods are implemented to increase oilproduction and recovery. The Inline Water Separation (IWS)technology has been developed as an alternative water


Inline Water Separation (IWS) Field PrototypeDevelopment and Testing

Authors: Dr. Jinjiang J. Xiao, Ramsey White, Dr. Shoubo Wang and Dr. Luis E. Gomez


handling technique. Envisioned is a compact separationtechnology based system that can be installed at a wellhead ordownstream of a manifold to knock out a significant portionof the produced water from a production stream. The systemwill process separated water to injection quality, to bedisposed of or re-injected to the reservoir on-site while theremaining lower water cut production stream continues to theGOSP via the existing gathering system.

WORKING PRINCIPLES

The IWS technology was developed through a phasedapproach. A mini-scale lab system was first designed andtested1. Following the encouraging results from the mini-scalelab test, a field prototype was developed to handle 10,000barrels fluid per day (BFPD) with a minimum water cut of60%. The targeted operation pressure was 450 psig and thetargeted temperature was 180 °F.

Figure 1 shows a schematic of the IWS system. It consistsof a Gas-Liquid Cylindrical Cyclone (GLCC) for gas-liquidseparation, a Liquid-Liquid Pipe Separator (LLPS) for first-stage oil-water separation, a Liquid-Liquid CylindricalCyclone (LLCC) for second-stage oil-water separation, a de-oiling Liquid-Liquid Hydrocyclone (LLHC-1) and a waterpolishing Liquid-Liquid Hydrocyclone (LLHC-2).

The GLCC

The gas-liquid separation is achieved with a compact gas-liquid separator. It is a dual-inlet GLCC, 16” in diameter

and 12 ft in height. Upstream of the GLCC, there is a 12”vertical inlet section, which is used to damp the slug flowand coalesces droplets and bubbles. The inclined dual inlethelps gas-liquid pre-separation, allowing the gas stream toflow through the upper tangential inlet and the liquidstream to flow through the lower tangential inlet. The upperinlet generates a gas vortex in the upper part of the GLCCand separates the liquid droplets from the gas stream. Thelower inlet generates a liquid vortex in the lower part of theGLCC and separates the gas bubbles from the liquid stream.Different control strategies can be deployed for liquid levelcontrol and pressure control, based on the operatingconditions. The gas stream is sent downstream of the IWSsystem, and the liquid stream is sent to the LLPS for first-stage oil-water separation. The gas stream can be eitherrecombined with the oil stream or sent directly to thedownstream production line.

The LLPS

The objective of the first-stage oil-water separation is toproduce a continuous oil stream and a continuous waterstream. The liquid phase (oil and water) with a small amountof entrained bubbles from the GLCC liquid outlet flows intoan LLPS. The LLPS is a pipe section that is 16” in diameterand 15 ft long. It promotes oil and water stratification andoil-water droplet coalescence inside the enlarged pipe. The gasbubbles serve as a gas floatation mechanism and carry oildroplets to the upper part of the pipe. The concentrated oilstream is removed from the top end of LLPS, while the


Fig. 1. Schematic showing the IWS working principles.


Table 1. Mass balance and performance calculation of the IWS system


water stream water cut (< 2%), achieved with a control valvein the oil discharge line and a water cut meter in the waterstream.

The LLHC

The de-oiling hydrocyclone is used to remove large oil dropletsfrom the water and produce a cleaner water stream with an oilconcentration of less than 200 ppm. The polishinghydrocyclone is used to remove smaller oil droplets from thewater stream and produce a clean water stream with an oilconcentration of less than 50 ppm for disposal. The waterstream from the LLCC is sent into the first-stage LLHC-1 forthe removal of most of the residual oil in the water. Thesecond-stage LLHC-2 is used to further polish the water tosatisfy the requirements for discharge or injection water quality(< 50 ppm oil concentration). The hydrocyclone reject flowsneed to be controlled within 10%, which is done bycontrolling the differential pressure ratios (DPRs). The oilreject flow is a low-pressure discharge stream, which may needto be either pumped back to the higher pressure productionstream or sent to a lower pressure tank. A pressure boostingpump upstream of the hydrocylones may be needed if theproduction stream pressure is low.

The performance of the individual compact separationcomponents of the IWS system has been well-defined in previousstudies2, 3. Based on the individual component separationefficiency, a mass balance and performance calculation studywas conducted on a base case IWS system, Table 1.

concentrated water stream exits from the bottom end of LLPS.The continuous oil stream, with minimum water content, issent to the down stream production stream for furtherprocessing, and the continuous water stream is sent to theLLCC for the next oil-water separation stage to further polishthe water quality for disposal. The control strategy for theLLPS is to maximize the water separation efficiency withminimum oil carry under in the continuous water stream. Awater cut meter and a control valve in the oil discharge streamare required to achieve this control for the LLPS. The typicaloil carry under allowed in the continuous water stream is10% to 15%, based on the second-stage oil-water separationrequirements.

The LLCC

The objective of the second-stage oil-water separation is toremove most of the oil from the continuous water stream,producing a water stream with 1% - 2% oil concentration forthe next stage: the de-oiling hydrocyclone. The LLCC is a pipe6” in diameter and 6 ft tall with a vortex finder at the upperpart and a cone at the lower part. It can handle a continuouswater stream with an oil concentration up to 15% and a waterseparation efficiency up to 90%. The continuous stream ofwater flows into the LLCC, which separates a significantportion of the oil and produces a water stream with an oilconcen tration of less than 2%. The key performance parameterfor the LLCC is the split ratio for the oil and water dischargeflows. The split ratio should be controlled by measuring the

Inletlet

Inlet Water CutInlet Water Cut 0.60.6 (fraction)(fraction)

Total Inlet Liquid Flow Rate (QI)Total Inlet Liquid Flow Rate (QI) 10,00010,000 (bpd)(bpd)

Total Inlet Gas Flow Rate (Qg)Total Inlet Gas Flow Rate (Qg) 10,00110,001

Material and Pressure Balance for Individual Separators and Overall SystemMaterial and Pressure Balance for Individual Separators and Overall System

SeparatorsSeparators GLCCGLCC LLPSLLPS LLCCLLCC LLHC-1LLHC-1 LLHC-2LLHC-2 SystemSystem

Oil Separation Efficiency (0-1)Oil Separation Efficiency (0-1) 00 0.80.8 0.970.97 0.90.9 0.90.9

Water Removal Efficiency (0-1)Water Removal Efficiency (0-1) 11 0.950.95 0.90.9 0.950.95 0.980.98

Inlet Liquid Rate (bpd)Inlet Liquid Rate (bpd) 10,00010,000 6,5006,500 5,1545,154 4,875.904,875.90 10,00010,000

Overflow Total (bpd)Overflow Total (bpd) 3,5003,500 1,3461,346 278.1278.1 99.699.6 5,223.705,223.70

Overflow Water (bpd)Overflow Water (bpd) 300300 570570 256.5256.5 97.597.5 1,2241,224

Overflow Oil (bpd)Overflow Oil (bpd) 3,2003,200 776776 21.621.6 2.162.16 3,999.803,999.80

Underflow (bpd)Underflow (bpd) 6,5006,500 5,1545,154 4,875.904,875.90 4,776.304,776.30 4,776.304,776.30

Underflow Water (bpd)Underflow Water (bpd) 5,7005,700 5,1305,130 4,873.504,873.50 4,7764,776 4,7764,776

Underflow Oil (bpd)Underflow Oil (bpd) 800800 2424 2.42.4 0.240.24 0.240.24

Underflow Water CutUnderflow Water Cut 0.880.88 1.001.00 1.001.00 1.001.00 1.001.00

Overflow Water CutOverflow Water Cut 0.090.09 0.420.42 0.920.92 0.980.98 0.230.23

Water Removal EfficiencyWater Removal Efficiency 0.950.95 0.900.90 0.950.95 0.980.98 0.800.80

Oil Contamination in Oil Contamination in

Water (ppm)Water (ppm) 492492 5050 5050

Note:Note:

Oil separation efficiency – fraction of oil separated from the inlet stream of the separator.Oil separation efficiency – fraction of oil separated from the inlet stream of the separator.

Water separation efficiency – fraction of water separated from the inlet stream of the separator.Water separation efficiency – fraction of water separated from the inlet stream of the separator.


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The inlet water cut is assumed to be 60%, and the total liquid

flow rate is 10,000 barrels per day (bpd). Based on an oil

separation efficiency of 80% and water separation efficiency of

95% in the LLPS4, the overflow rate is 3,500 bpd with a water

cut of 9% and the underflow rate is 6,500 with a water cut of

88%. Assuming the LLCC oil separation efficiency is 97% and

water separation efficiency is 90%5, the LLCC overflow rate is

1,346 bpd with a water cut of 42%, and underflow rate is 5,154

bpd with a water cut of almost 100% (oil <1%). The first-stage

LLHC-1 can achieve a 492 ppm water quality, and the second-

stage LLHC-2 further polishes the water to about 50 ppm.

The overall water separation efficiency is defined as thepercentage of water removed from the total water production.As demonstrated in Table 1, the overall water separationefficiency of the IWS system is about 80% and the water cutin the production stream is reduced from 60% to 23%.

FIELD TRIAL TEST RESULTS

The field prototype system, Fig. 2, was installed upstream of aproduction test separator inside a GOSP. To minimize potentialinterruption to GOSP operations, all separated streams wererecombined after the IWS and sent to the test separator.

Testing Conditions

The field tests were conducted with a total of eight high watercut wells. The well test data are shown in Table 2. The wellwater rate ranges from 2,410 bpd to 7,470 bpd, and the oilrate ranges from 1,600 bpd to 4,080 bpd. The water cutranges from 52% to 82%. The tests were conducted witheither a single well or a combination of wells to provide theflow conditions needed for the performance evaluation.


Fig. 2. Photo of the IWS system installed at the test site.

Fig. 3. Typical well producing rate for testing.

Table 2. Well test data

Well Water Water Oil Rate Gas RateNumber Cut Rate (MBD) (Mscfd)

(%) (MBD)W1 81.7 7.2 1.6 1,300W2 54.6 4.24 3.52 2,450W3 64.7 7.47 4.08 3,200W4 59 2.7 1.9 1,400W5 72 4.8 1.9 1,400W6 52.4 2.41 2.2 2,200W7 56.4 3.5 2.7 1,800W8 53.1 3.1 2.8 2,200


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of 3 psi across the GLCC. The gas mass flow rate, measuredby a mass flow meter (FIT-101), and is 61 pound-mass/minute(lbm/min). The gas control valve (PV101) opening is 12%.The LLPS (LLPS-002) pressure is 367 psi (PI-103).

The LLCC (LLCC-003) underflow is 65% of the total inletflow, and the mass flow meter (FIT-103) reads a flow rate of6,561 bpd of water stream with a density of 63.14 lbm/ft3.The water cut meter (WCT-101) reads 99% of water cut, andthe control valve (LV-101) opening is 97%. The LLCC(LLCC-003) overflow rate measured by a mass flow meter(FIT-102), is 3,597 bpd. The overflow control valve (LV-102)opening is 26%. The LLCC overflow combines with the gasstream with a downstream pressure (test trap pressure) of 228psi (PI-105).

The LLHC-1 (LHC-004) DPR is measured by twodifferential pressure transducers from the inlet to the rejectflow discharge, and from the inlet to the water discharge flow.The DPR (DPRIC-101) is controlled at 2.49, and the controlvalve (DPV-101) opening is 92%. The LLHC-2 (LHC-005)DPR is 2.49 and the control valve (DPV-102) opening is 90%.The combined reject flows from both LHC-1 and LHC-2 aresent to the downstream test trap with a pressure of 227 psi(PI-106). The separated clean water rate, measured by a massflow meter (FIT-104), is 5,560 bpd. A control valve (PV-102)is used to control the water discharge line back pressure,which is measured by a pressure transducer (PI-104) at 331psi, to control the DPR of both LHCs. The separated water is

The IWS was fully instrumented with pressure, temperature,flow rates and water cut measurements to evaluate theperformance of the individual separation components and theoverall water separation efficiency of the IWS system. Figure 3shows typical real-time production rates during the testing. Theupper line is the water rate (around 6,000 bpd); the lower lineis the oil rate (around 2,000 bpd); and the middle line is the gasrate (around 1.2 million standard cubic feet per day (MMscfd)).

Computer Program and Data Acquisition

The IWS is equipped with a high performance Siemensprogrammable logic controller (PLC), which can be operatedat high environmental temperatures. The control strategy andthe data acquisition are programmed locally using the PLC.The PLC is also interfaced with the plant distributed controlsystem (DCS) for emergency shutdown, and linked with alaptop computer in the central control room. A computerprogram is developed using WinCC (a PC-based operatorcontrol and monitoring system) for remote display, controland data acquisition.

Figure 4 is a snapshot of the computer screen during a test,showing the IWS instrumentation, controls and dataacquisition program. It displays all the IWS components andthe operating conditions. For this case, the inlet pressure is378 psi (PI-101) and the inlet temperature is 162 °F (TT-101).The GLCC (GLCC-001) level is 86.9% (LT-101), and theGLCC pressure is 375 psi (PI-102), indicating a pressure drop

Fig. 4. IWS flow diagram and computer program.


combined with the main production stream and sent to thetest trap.

Water Samples and Lab Analysis Results

Water samples were taken during the test from the LLCCwater exit, LLHC-1 and LLHC-2, for evaluation of the waterseparation purity. Figure 5 shows a photo of the watersamples collected during one of the tests. The bottle on theright side is the water sample from the LLCC water discharge.It looks dirty and contains 2% of oil. The bottle in the middleright is the water sample from the first-stage hydrocyclonewater discharge. The oil concentration is about 100 ppm. Thebottle in the middle left is the water sample from the second-stage hydrocyclone water discharge. The oil concentration isabout 30 ppm. The bottle on the left side is the water sampletaken from the water-oil separator (WOSEP) water dischargein the GOSP. The oil concentration is about 35 ppm. Thewater sample from the IWS outlet looks clearer than the watersample from the WOSEP, even though they have similar oilconcentrations. It indicates that the IWS separates most of thesuspended solids from the produced water, and that the wateris less contaminated by other additives. It further indicatesthat it is preferable to separate the water upstream of theGOSP at the higher pressures and temperatures on-site.

Overall IWS Performance in Terms of Water Purity andSeparation Efficiency

The overall performance of the IWS was evaluated by the waterseparation efficiency and the water purity achieved with thesystem. The water separation efficiency is defined as thepercentage of the separated clean water over the total inletwater. The purity of the water is defined as the oilconcentration in the separated clean water. Figure 6 shows theoverall IWS performance for all the testing conditions. The datapoints in red plot the oil concentration of the separated water(in the secondary y-axis) vs. the inlet water production rate.The data points in blue plot the total water removal efficiency(in the primary y-axis) vs. the inlet water production rate. It canbe seen that the oil concentration range for the separated cleanwater is from 10 ppm to 35 ppm. The overall water removal

efficiency range is from 50% to 80%. It is noted that the waterquality is around 10 ppm for the inlet flow rate range from5,000 bpd to 10,000 bpd, which is the design range for thepilot IWS system. When the inlet flow rate is lower (< 5,000bpd), it is not high enough to drive the hydrocylones to achievethe best separation. On the other hand, when the inlet flow rateis higher (>10,000 bpd), the hydrocylones’ separation efficiencyis slightly lower due to their limited operating range. In general,the separation efficiency is higher for higher inlet water cuts.

System Pressure Drop

The GLCC, LLPS and LLCC separation components don’trequire a significant pressure drop. The total pressure lossthrough these components is within 5 psi. With the IWS,therefore, there is no significant energy loss for the main flow(gas + oil + reduced water flow). The hydrocyclone is thedominant device that requires a significant pressure drop tomaintain the proper differential pressure ratio. Figure 7 showsthe overall pressure drop across the IWS system for all thetested flow conditions. The pressure drop range is from 10 psiat lower flow rates (4,000 bpd to 6,000 bpd) to 110 psi athigher rates (12,000 bpd). It can also be noted that the pressuredrop is quite linear for the best operating range (5,000 bpd to10,000 bpd), and the pressure increases exponentially when the


Fig. 5. Water samples.

Fig. 6. IWS separation efficiency and oil in water concentration in ppm.

IWS Performance

0

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Pressure Drop vs. Flow Rate

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drop occurs. Better water quality monitoring technologiesneed to be identified.

POTENTIAL APPLICATIONS

As an oil field matures, water production will increase and waterhandling can become a pressing issue. The IWS technology offersthe benefits that include expanding the system water handlingcapacity, minimizing the number of dead wells, delaying artificiallift and reducing flow line corrosion.

Three potential IWS applications were identified:

• Debottlenecking a GOSP for greater produced waterhandling capacity. One potential solution to increasewater handling capacity in the GOSPs is to tie the IWSto the production header at the GOSPs to separate thewater before it enters the high-pressure production trap.Separated water can be transferred directly to the inletof the injection pump.

• Offshore production water removal for direct disposalor injection. A skid-mounted IWS unit can be installedon a tie-in platform, knocking out a significantpercentage of the water from the production stream andinjecting the separated water into a re-completed deadwell for direct disposal. The remaining lower watercontent production stream can then be transported toshore via the existing trunk lines.

• WOSEP application for water quality improvement.With high oil demand, a WOSEP’s performance candeteriorate below an acceptable limit. An IWS unit canbe installed in parallel to the WOSEP, taking some ofthe load from the vessel and ensuring its performancewithin the plant specifications.

• Remote manifold installations. For wells with lowwellhead pressure and high GOSP pressure, installationof an IWS unit at a remote manifold can reduce wellback pressure and increase well production. Theseparated water can be sent to the water injectionsystem nearby.

CONCLUSIONS

The following conclusions can be made based on theengineering studies and the field test results:

1. A fully instrumented, compact, multiphase IWS prototypehas been developed and successfully tested under fieldconditions.

2. It has been demonstrated that the IWS can separate asignificant portion of the produced water from aproduction stream. The water cut range of the productionstream that has been tested is from 50% to 80%. The fieldtest results show that the overall water separationefficiency is around 70%.

rate is higher than 10,000 bpd. If the liquid rate is too low, thehydrocyclone efficiency is lower. If the liquid flow rate is toohigh, the pressure drop will be too high.

SUGGESTED FUTURE IMPROVEMENTS

Oil-Water Emulsion Effect

During the trial test, water-in-oil emulsion was observed inmany of the wellhead samples. For wells without emulsion,the IWS produces high quality water, while for wells withemulsion, the water quality deteriorates, however, waterquality improves when a demulsifier is injected upstream ofthe IWS unit. It therefore is recommended that a demulsifierbe injected, preferably at the wellhead, to break up the oil-water emulsion in the flow line. To improve the emulsionhandling capabilities of the IWS, a redesign of the LLPS isneeded, incorporating emulsion breaking techniques, such asthermal, chemical, mechanical or static electrical treatment. Itshould be pointed out that the IWS technology is meant forhigh water cut applications and emulsions should not be asmuch of an issue at very high water cuts. Moreover, the IWScan be installed in such a way that shearing through the chokevalves and trunk lines can be minimized.

Excessive Slugging Effect

Severe gas-liquid slugging was observed from the trunk lineduring the testing. A large flow line diameter, uneven terrain,long distances and low production rates all contribute to theformation of severe slugging. It was also observed that watercut varies in a slug body during slugging. It is recommendedthat a slug handling module, such as a slug catcher or a slugdamper, be installed upstream of the IWS unit to improve itsslug handling capability. Enlarging the size of the LLPS can alsoenable the system to be more tolerant of slugging conditions.

Inline Real-Time Water Quality Monitoring and Control

One of the advantages of the IWS design is the utilization ofinline real-time water quality monitoring and controls tooptimize the operation. After careful consideration in thedesign stage, a microwave water cut meter, a Micro-Motionmass flow meter with density measurement capability and aMoniTech oil-in-water monitor were chosen for the field testunit. During the testing, it was observed that the microwavewater cut meter was sensitive to the salinity of the water.Manual calibration was required when the water salinitychanged during the testing. Usually, the salinity of theproduced water doesn’t change much during part of theproduction period. For some applications, though, the watersalinity may vary from well to well. An inline salinitymeasurement will be required to calibrate the microwavewater cut meter. It was also observed that the measurement ofthe MoniTech oil-in-water monitor was affected by gasbubbles from the dissolved gas when a significant pressure


3. The test results also show that the IWS produces highquality water, which can be injected back into the disposalwells or disposed of directly. The oil-in-water concentrationof the separated water is from 10 ppm to 35 ppm, underthe testing conditions.

ACKNOWLEDGMENTS

The authors thank Saudi Aramco management for supportand permission to present this article. This project would notbe possible without the commitment and assistance providedby numerous engineers from multiple Saudi Aramcoorganizations.

This article was prepared for presentation at the Abu DhabiInternational Petroleum Exhibition and Conference, AbuDhabi, U.A.E., November 1-4, 2010.

REFERENCES

1. Wang, S., Gomez, L.E., Mohan, R.S., et al.: “CompactMultiphase Inline Water Separation (IWS) System - A NewApproach for Produced Water Management andProduction Enhancement,” SPE paper 104252, presented atthe SPE International Oil and Gas Conference andExhibition, Beijing, China, December 5-7, 2006.

2. Shoham, O. and Kouba, G.E.: “The State-of-the-Art ofGas-Liquid Cylindrical Cyclone Compact SeparationTechnology,” Journal of Petroleum Technology, July 1998,pp. 54-61.

3. Wang, S., Gomez, L.E., Mohan, R.S., Shoham, O., KoubaG.E. and Marrelli, J.D: “The State-of-the-Art of Gas-Liquid Compact Separator Control Technology - From Labto Field,” proceedings of the 8th International Symposiumon Gas-Liquid Flows: ASME/JSME Joint FluidsEngineering Division Summer Meeting, Honolulu, Hawaii,July 6-10, 2003.

4. Perez, C.: “Horizontal Pipe Separator – Experiment andModeling,” Ph.D. Dissertation, the University of Tulsa,Tulsa, OK, 2005.

5. Escobar, O.M.: “Performance Evaluation of a ModifiedLLCC Separator,” M.S. Thesis, the University of Tulsa,Tulsa, OK, 2005.




Dr. Luis E. Gomez is the Vice Presidentand Chief Development Officer atMultiphase Systems Integration (MSI),USA. He also serves as a VisitingAssistant Professor and a SeniorInvestigator at the Tulsa UniversitySeparation Technology Projects

(TUSTP), an industry consortium of 15 companies includingChevron, ExxonMobil, Cameron, Petrobras, StatoilHydro,Saudi Aramco, and Shell. TUSTP receives funding from theNSF, DOE, OCAST (Oklahoma Center for theAdvancement of Science and Technology) and TU-CoRE(Tulsa University/Chevron Center of Research Excellence).

Luis has over 15 years of experience in both academiaand the oil and gas industry, and is prominently recognizedfor his expertise in oil/water/sand/gas production,transportation and separation, multiphase compactseparators, multiphase dispersions and uncertainty analysisin multiphase flow phenomena. He has conducted andsupervised more than 100 projects related to multiphaseflow production sponsored by major companies.

Luis has published more than 15 papers on thesesubjects in peer-reviewed international journals, and isregularly invited to speak and teach courses at leadingconferences and seminars worldwide.

He received his B.S. degree in Mechanical Engineeringfrom the Universidad de Los Andes, Merida, Venezuela, andhis M.S. and Ph.D. degrees in Petroleum Engineering fromthe University of Tulsa, Tulsa, OK.

BIOGRAPHIES

Dr. Jinjiang J. Xiao is a PetroleumEngineering Specialist with theProduction Technology Team of theExploration and Petroleum EngineeringCenter - Advanced Research Center(EXPEC ARC). His interests are wellproductivity improvement and water

management. Prior to joining Saudi Aramco in 2003, Jinjiang spent 10

years with Amoco and later with BP-Amoco working onmultiphase flow, flow assurance and deepwater productionengineering.

He received his M.S. and Ph.D. degrees in PetroleumEngineering, both from the University of Tulsa, Tulsa, OK.

Ramsey White has been with theProcess Engineering Group of theNorth Ghawar Producing Departmentsince August 2007.

He has 11 years of processengineering experience, mostly withSNC-Lavalin in Houston, TX, and has

been involved in several process studies and design projectsfor upstream and downstream facilities.

Ramsey received his B.S. degree in Chemistry from theAmerican University in Cairo, Egypt, and his M.S. degree inChemical Engineering from the University of Houston,Houston, TX.

Dr. Shoubo Wang is the Vice Presidentand Chief Operating Officer atMultiphase Systems Integration (MSI),USA. He also serves as a VisitingAssistant Professor and a SeniorInvestigator at Tulsa UniversitySeparation Technology Projects

(TUSTP), an industry consortium of 15 companies includingChevron, ExxonMobil, Cameron, Petrobras, StatoilHydro,Saudi Aramco and Shell. TUSTP receives funding from theNSF, DOE, OCAST (Oklahoma Center for theAdvancement of Science and Technology) and TU-CoRE(Tulsa University/Chevron Center of Research Excellence).

Shoubo has over 20 years of experience in bothacademia and the oil and gas industry, and is prominentlyrecognized for his expertise in multiphase flow,measurements, instrumentation/controls, oil and gasprocessing and production facilities, and compactseparators.

He has published more than 20 papers on these subjectsin peer-reviewed international journals and conferenceproceedings.

Shoubo received his B.S. degree in MechanicalEngineering from the University of Petroleum, Beijing,China, and his M.S. and Ph.D. degrees in PetroleumEngineering from the University of Tulsa, Tulsa, OK.


ABSTRACT

Gas condensate wells within the Khuff carbonate formation inSaudi Arabia are typically acid fracture stimulated followingdrilling. Pressure transient tests conducted after stimulationindicate excellent fracture conductivity and reasonable lengths.After tie-in, the wells experience some early productionchange, which stabilizes after 3 to 5 months. The producingbottom-hole pressure (BHP) of many such wells also falls nearor below the dew point pressure during this period.

This article identifies some reasons for this production andpressure behavior. Possible causes of the early rate changefollowed by stabilization, which include near well liquiddrop-out, fracture conductivity deterioration with increasedeffective in-situ stresses, reservoir permeability loss also dueto increased effective stresses and pressure drop, and non-Darcy flow, were investigated. This article presents thechanging total skin with time and allocates it to each of thepotential damage mechanisms. This was accomplished using afull geomechanics and reservoir simulation model, whichtreats multiphase flow effects and stress-dependent matrixpermeability as well as fracture conductivity. By doing so, theproduction behavior was matched and the main controllingfactors were identified.

This article introduces a generalized skin factor equationcombining all major variables controlling production,separates individual terms with respect to well rates andidentifies the main mechanism impacting production behavior.

INTRODUCTION

An analysis of the long-term production behavior of acidfracture stimulated gas condensate wells was conducted andincluded the following primary functions: (1) Review andmodel the production performance of six wells in the gascarbonate reservoir in Ghawar, and (2) Determine thecontributions of various reservoir and fracture parameters onthe productivity behavior.

The primary goal of modeling the historical productionbehavior of the six vertical wells (SA-1 through SA-6) was toidentify the mechanisms impacting productivity change withtime. After the wells were drilled and completed, an acidfracture treatment was performed on each well and the wells

were cleaned up. After cleanup but before tie-in, multirateflow and buildup tests were performed on the wells. A two-year production period was investigated and used for historymatch purposes.

The production behavior response of all wells exhibitedhigh initial productivity index (PI) prior to rate stabilization.The changes of PI over time could be attributed to a numberof reservoir flow and fracture mechanisms. These include:

1. Condensate dropout as the producing well pressure islowered. This dropout has the effect of lowering theeffective permeability of the fracture and the reservoirnear the well due to two-phase gas-condensate floweffects.

2. Changing conductivity of the acid fractures as thepressure declines and the effective stress on the fracturesincreases.

3. Changing permeability of the reservoir as the pressure islowered and the effective stress increases.

4. Turbulence in either the fracture or reservoir in the nearwellbore region.

The process of matching the Pressure Transient Analysis(PTA) tests and the long-term production performance allowedinsight to comprehend these effects and identify which of theseeffects were important. Understanding the reservoir andmodifying the appropriate variables also somewhat resolvedthe uniqueness of the problems related to history matches.

STUDY PROCEDURE

The following were pursued during the study:

• Candidate well properties assessment and well selection.

• Flow and buildup test analysis (PTA).

• Long-term production analysis.

• Reservoir simulation and matching.

• Skin allocation.

• Sensitivity runs to determine controlling factorsgoverning production.

During the course of the study, several experimental datapoints were included: reservoir geomechanical properties (kand φ as functions of stress), acid fracture conductivity with


Analysis of Long-Term ProductionPerformance in Acid Fractured CarbonateWells

Authors: Dr. Zillur Rahim, Mahbub S. Ahmed and Adnan A. Al-Kanaan



significant. An order of magnitude drop in conductivity canoccur for wells flowing at higher rates. These conductivityreductions should be readily apparent in PTA tests or in thelong-term performance of such wells.

Four of the six wells analyzed had multirate tests per -formed on them, and all of the wells had buildups followingthe last rate. Gas flow rates during the tests were typically onthe order of 30 MMscfd to 40 MMscfd. None of the wellsexhibited any turbulence effects during any of the flow orbuildup periods of the test. This probably shows the highlyconductive nature of the fractures. Therefore, no turbulenceeffect was incorporated in the models.

Stress-Dependent Acid Fracture Conductivity

For wells that have been acid fractured, it is well establishedthat the conductivity lessons with time as the well is produced.The conductivity is reduced due to damage and/or fractureclosure depending upon the rock type. A conductivity stressrelation provided in the following equations, was establishedon some carbonate cores:

log Cf1 = –k1 (σh – C1): upper bound conductivity, and log Cf2 = –k2 (σh – C2): lower bound conductivity,

where ki and Ci are experimentally derived coefficients, andk2>k1, and σh represents the horizontal effective stress. Thesecurves are normalized by dividing by a reference state. In thisway, the equations can give fracture conductivity multipliersas a function of stress. Figure 1 shows the two normalizedcurves as a function of effective horizontal stress.

The Nierode-Kruk method5, 6 has been used extensively inthe past to estimate acid fracture conductivity. The Nierode-Kruk conductivity calculation is dependent on rock embed mentstrength. Figure 1 also shows the Nierode-Kruk conductivitycalculated for an embedment strength of 40,000 psi ascompared to experimentally derived values.

It is clear that the Nierode-Kruk values predict much greaterfracture conductivity loss than either of the Saudi Aramco curves.By adjusting the value of the embedment strength used in theNierode-Kruk method, it is possible to exactly duplicate theexperimental bounding curves. The lower bound conductivitycase was used as a starting point in the simulation models.

stress, acid fracture parameters, pressure-volume-temperature(PVT) characterization, and relative permeability.

CARBONATE RESERVOIR PROPERTIES

Six wells were selected for the study. These six wells exhibitedsome post-frac productivity changes during the first 6 monthsof production. (It has to be mentioned that this initial pro -ductivity change is not a general phenomenon for all Khuffwells.) Reservoir and fracture data, post-frac early flow andbuildup data, long-term production data and petrophysicalanalysis were available in the selected wells. Table 1 presentssome of the basic reservoir properties.

MAJOR FACTORS AFFECTING PRODUCTIONBEHAVIOR

As mentioned in the introduction, the three major factors thatwere investigated and used as input data, while quantifyingproduction behavior from the wells and history matching therate and pressure profiles, include: (1) non-Darcy effects onproduction, (2) pressure and in-situ stress effects on fractureconductivity and reservoir permeability, and (3) liquid dropoutas pressure reaches dew point. The following sections presentsome of the conclusions derived from the study.

Turbulence (Non-Darcy) Effects

Reservoir turbulent flow (non-Darcy flow) is a short-rangephenomenon that acts as an additional rate dependent positiveskin. It is most pronounced in radial flow, high-rate wells.Typically, the introduction of a second mobile phase, such as acondensate, can increase the turbulence coefficient by an orderof magnitude over the single phase turbulence coefficient1.Successful acid fracturing of the well significantly reduces thereservoir turbulence. This is because the fractures create aneasy pathway for the gas to flow from the reservoir to thewellbore. Once the high flow rate is transferred to thefracture, linear flow occurs from the fracture to the well. Asper design, acid fractures induced in the study wells are inexcess of 100 ft in half-length. This length significantlynegates turbulence effects.

Turbulence can also occur within the fracture where flowvelocities are significantly high. Moreover, multiphase floweffects aggravate turbulence in fractures. Laboratory datadeveloped by Stim-Lab2, 3 for propped fractures, plus someadditional correlative work by Martins4 and others, show thatconductivity drops in proppant treatments are quite

Table 1. Selected gas well reservoir properties

SA-1 SA-2 SA-3 SA-4 SA-5 SA-6Gross Pay (ft) 120 160 150 155 88 160Net Pay (ft) 62 85 100 65 25 80φ (frac) 0.13 0.10 0.12 0.09 0.12 0.10Sw (frac) .021 0.2 0.21 0.24 0.29 0.32k, md 2.7 1.5 3.6 1.7 2.9 1.2

Fig. 1. Bounding normalized acid fracture conductivity.


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Figure 2 shows the normalized acid fracture conductivitymultipliers as a function of core pressure. Increased reservoirpressure improves the state of in-situ stress on a fracture,thereby preserving higher conductivity. Once again, theNierode-Kruk relationship is also shown in this figure.

Figure 3 shows the normalized conductivity multipliers forthe low conductivity case varying with decreasing reservoirpressure. The multiplier at the initial average pressure of7,500 psi and a bottom-hole pressure (BHP) of 2,000 psi isabout 0.12. The effects of in-situ stress and reservoir pressureare incorporated in the simulation model.

Stress Dependent Reservoir Permeability

Stress dependent reservoir permeability and porosity equationsderived from lab data7 are:

kres=c1xkambientx(σ- +C2)-c3

φres=c4xφambientx(σ- +C5)-c6

where the subscript ambient refers to the zero stress state, σ-

for mean effective stress, and k and φ refer to permeability(md) and porosity (fraction), respectively. Ci represents theexperimentally derived coefficients. The mean effective stressis the mean total stress minus the pressure, expressed as:

where α is Biot’s constant, and τ is the total stress.This reservoir permeability data, normalized to the mean

effective stress of 5,000 psia (associated with a reservoirpressure of 7,500 psi), is shown in Fig. 4.

Also shown in this figure is the experimentally derivedcorrelation from the Core Lab study2. Such data were

included in the simulation model while running it in fullgeomechanics mode. In this case, the model is calculatingstresses directly, and the appropriate permeability multiplierwas determined from the mean effective stress.

The core stress dependent data can be made a function ofthe pressure within the core, assuming a reasonable set ofboundary conditions for the stress calculation. Normalizedresults are shown in Fig. 5 where the reference point is theinitial reservoir pressure of 7,500 psi.

As the reservoir pressure declines, the mean total stressalso declines. This is observed in the field, in that thefracturing pressure at the well decreases if the well is re-stimulated at a later point in time after depletion has takenplace. The decrease in mean total stress is smaller than thedecrease in average pressure by a certain factor, andtherefore the effective stress in the far field increases andpermeability decreases.

The situation is different around the wellbore. If a wellflows at a constant BHP, then the maximum mean effectivestress at the well occurs at time zero and decreases as theaverage pressure declines. Therefore, the permeability firstdrops, due to initial drawdown, and then slowly recoversduring depletion. For the subject wells, the minimum flowingBHP is around 2,000 psi. The maximum reservoirpermeability multiplier in this case will be around 0.77.

A laboratory derived porosity equation relating φres toφambient was previously shown. A simpler approach was takento represent porosity impact. Pore volume compressibility wascalculated from the porosity equation. An average value of


Fig. 2. Acid fracture conductivity dependence on pressure.

Fig. 3. Acid fracture conductivity with decreasing pressure.

Fig. 5. Permeability dependence on pressure.

Fig. 4. Permeability dependence on mean effective stress.



A history match PTA test was then performed with thereservoir simulation model. The well drainage area wasdetermined from production analysis. The long-termproduction performance was then history matched byadjusting drainage area, krg, and dew point pressure. The totalskin factor defined by the summation of skin due to liquiddropout, permeability loss, and conductivity loss was nextdecoupled. Sensitivity runs were made using multilayer modelswith full or no geomechanics (radial models were also used).

Base Pressure Transient Analysis

Measurements during several flow and shut-in periods wereperformed for Well SA-1, as given in Table 2. Each portion ofthe data was analyzed.

A history match was performed; Fig. 7 presents the flowand buildup test match and Fig. 8 presents the pressure andderivative match. This match was very reasonable usingaverage fracture parameters of 100 ft and the Fcd of 100,which were close to the original design parameters. The matchwas obtained using zero turbulence and multipliers for bothreservoir permeability and fracture conductivity. The reservoirproperties, such as kh and Pavg, were all within an acceptablerange of accuracy.

Turbulence effects were not present in the data. Forstimulated wells experiencing linear flow and turbulence,there tends to be a hump in the early time derivative aftercoming out of wellbore storage and transitioning into linearflow. This hump is similar to the hump in a radial flow

3.0 x 10-6 1/psi gave the best fit for porosity ratio vs. pressure.The gas compressibility from the PVT data was estimated to be79.0 x 10-6 1/psi, which is an order of magnitude larger thanthe pore volume compressibility. Therefore, the pore volumecompressibility is small in comparison and there was no needto account for it by using a more complex stress formulation.A value of 3.0 x 10-6 1/psi for pore volume compressibility wasused in the study. The change in porosity within the model isthen calculated in a manner analogous to a conventional blackoil simulation model.

Liquid Dropout

As the pressure goes below the dew point, the liquid (conden -sate) drops out in the reservoir, particularly near the wellbore,thereby causing a reduction of relative permeability to gas. Thiscauses a decrease in production. Simulation studies have shownthat the near well condensate saturation can quickly reach over30% within 200 ft to 300 ft of the radius, Fig. 6.

The effect of liquid dropout was modeled based onreservoir and fluid properties for history matching pressureand production behavior.

Analysis Procedure

The model was first built considering petrophysics and otherreservoir and geological factors. Next, permeability, k,dimensionless fracture conductivity, Fcd, and fracture half-length, Lf, were estimated from stimulation analysis andreservoir data. The reservoir simulation model was then builtwith kres = f(pavg,p) and Cf = f(pavg,p), and with liquid effectson gas relative permeability, krg.

Fig. 6. Liquid dropout profile near wellbore.

Fig. 8. Pressure and derivative history match.

Fig. 7. Flow and buildup test history match.

Table 2. Flow and buildup test for Well SA-1

Flow Period Duration, hours Gas Rate, MMscfd1 25 302 18 403 25 04 1.7 365 0.3 06 8 307 17 368 60 0


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model when there is a positive skin. In practice, this humpcan also be interpreted as fracture face skin or to a “choked”fracture8. Also, the conductivity to match both the last flowperiod and the buildup is very high, indicating an infiniteconductivity fracture. This is inconsistent with a turbulentflow situation. Qualitatively we can state that turbulencedoes not appear to be present in the test.

The residual oil saturation, Sorg, which has a verysignificant impact on late time performance, does not matterin the interpretation of this well test (or, for that matter, inany of the other well tests to be analyzed). The amount ofproduction below the dew point is insignificant.

Productivity Analysis

The gas rate, cumulative production and pressure behaviorhistory for Well SA-1 are provided in Figs. 9 and 10. Thefollowing controlling parameters were used to match theproduction and pressure data for this well, Fig. 11.

• Liquid drop out near the fracture.

• Reservoir permeability reduction.

• Conductivity loss in the fracture.

For the match shown in Fig. 11, Fig. 12 presents the reservoirpressure and PI vs. time for Well SA-1, assuming a drainage areaof four square miles. The PI is constant at late times at a skin of-0.5. The total skin factor with time is presented in Fig. 13. Theflowing material balance (FMB) plot9 was used to determine thedrainage area and all variables presented in these figures contri -buted to the match shown in Fig. 11.

Partitioning of Skin Effects

The skin factors are time dependent and can be combined torepresent the pseudo-total skin, ΔS’(t), given by the equation:ΔS’(t) = ΔSdropout(t) + Δ Spermloss(t) + ΔS’(t). Due to thenonlinearity of the skin factors, they are not additives, andtherefore the pseudo-total skin, ΔS’(t), is only an approx -imation of the true total skin, ΔS’(t).

It now remains to determine the relative significance of thethree identified productivity loss mechanisms in the match.The goal is to quantify the skin increase of each of theconstituent parts with time. The following variations of thehistory match were performed to ascertain the contributionfrom the individual variables:

1. Remove the liquid by setting the solution’s oil-gas ratio to0.0 (remove liquid damage).


Fig. 10. Pressure history, Well SA-1.

Fig. 9. Rate history, Well SA-1.

Fig. 12. PI and reservoir pressure vs. time, Well SA-1.

Fig. 13. Total skin factor and BHP, Well SA-1.

Fig. 11. History match pressure data for observed production.



for the wells we studied does not contribute to additional skindamage. This is because the remaining conductivity after theloss still provides a high value of Fcd. In one example studied,the initial Fcd of the fracture was 110. The fracture conduc -tivity multiplier at this later time was 0.15 and the krg in thefracture was 0.85 for a combined multiplier of 0.13. Thecalculated Fcd was therefore 14, which is still high, and thefracture has near infinite conductivity characteristics, however,if Fcd drops below a certain limit, the well will start behavinglike a well without any stimulation treatment, which willadversely affect productivity and gas recovery.

The skin allocations for Wells SA-1 to SA-5 are provided inTable 3. Only one well shows some damage in the inducedfracture that contributes to the rate behavior of the well.

CONCLUSIONS

A few possible mechanisms impacting production behaviorhave been investigated in this study. Those include: (1)Turbulence or non-Darcy effects, (2) Fracture conductivity lossdue to increasing effective stress at the well, (3) Reservoir per -meability loss due to increasing effective stress fromproduction at the well, and (4) Condensate dropout aspressure drops below the bubble point. The followingconclusions can be drawn based on our investigation and arelimited to the particular field and well configuration only.

1. Although, in proppant fractured wells, the skin, due toturbulence or non-Darcy effects is dominant, it was foundto be unimportant for the acid fractured cases.

2. The overall productivity behavior was quantified asdependent on the changing skin factor and as a function oftime for each well. This skin was allocated to the followingthree main mechanisms during history matching: fractureconductivity, reservoir permeability and liquid dropout.

3. The majority of the skin increases occur in the first fewmonths of production, and stability is reached after that time.

4. A few wells whose early production data indicated aninitial decline followed by production stabilization weremostly impacted by the liquid dropout and effects of in-situstress and pressure on reservoir permeability. Moreassessment with longer term production performance isneeded to confirm the initial results.

2. Remove the pressure-dependent reservoir permeability only(remove reservoir permeability loss).

3. Make the fracture conductivity constant at the initialfracture conductivity (remove fracture conductivity losswith pressure).

4. Remove all of the above effects in one final run (nodamage case).

For each run everything else was maintained at the historymatch values. A chart of the BHP vs. time for these cases isgiven in Fig. 14.

The calculated pressure is virtually identical for the historymatch and the case with constant fracture conductivity.Therefore, the fracture conductivity loss does not contributeto an increase in skin. This is because the fracture conductivitystill remains high at the end of the production period.

The skin effects can be normalized to the sum of theconstituent parts and are shown for an example well in Fig.15. Liquid dropout accounts for the highest increased skin;geomechanical reservoir permeability loss accounts for amoderate contribution to increased skin; and the fractureconductivity loss accounts for the lowest incremental skin.Skin is directly proportional to the additional pressure dropassociated with a particular damage mechanism.

The fracture conductivity loss during the production time

Fig. 14. Skin effects on pressure behavior.

Fig. 15. Normalized skin allocation for an example well.

Table 3. Skin allocations for gas well deliverability

Well Total % Skin % Skin % Skin dueSkin due to due to k to Cf Loss

Liquid DecreaseDropout

SA-1 4.1 64 35 1SA-2 4.8 64 35 1SA-3 1.6 51 48 1SA-4 1.54 48 48 2SA-5 6.2 36 28 35


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5. When fracture conductivity degradation is not an issue,and the presence of induced fracture is confirmed, re-fracturing cannot be recommended for productivityrestoration.

ACKNOWLEDGMENTS

The authors would like to thank the management of SaudiAramco for permission to publish this article.

This article was prepared for presentation at the SPEAnnual Technical Conference and Exhibition, Dallas, Texas,October 9-12, 2005.

REFERENCES

1. Lombard, J.M., Longeron, D. and Kalaydjian, F.:“Influence of Connate Water and Condensate Saturationon Inertial Effects in Gas Condensate Reservoirs,” SPEpaper 56485, presented at the SPE Annual TechnicalConference and Exhibition, Houston, Texas, October 3-6,1999.

2. Penny, G.S. and Jin, L.: “The Development of LaboratoryCorrelations Showing the Impact of Multiphase Flow,Fluid, and Proppant Selection upon Gas Well Productivity,”SPE paper 30494, presented at the SPE Annual TechnicalConference and Exhibition, Dallas, Texas, October 22-25,1995.

3. Jin, L. and Penny, G.S.: “A Study of Two-Phase, Non-Darcy, Gas Flow through Proppant Pacs,” SPE Production& Facilities, Vol. 15, No. 4, November 2000, pp. 247-254.

4. Martins, J.P., Milton-Taylor, D. and Leung, H.K.: “TheEffects of Non-Darcy Flow in Propped HydraulicFractures,” SPE paper 20709, presented at the SPE AnnualTechnical Conference and Exhibition, New Orleans,Louisiana, September 23-26, 1990.

5. Nierode, D., Williams, B.B. and Bombardieri, C.C.:“Prediction of Stimulation from Acid FracturingTreatments,” Journal of Canadian Petroleum Technology,Vol. 11, No. 4, October - December 1972, pp. 31-41.

6. Williams, B.B., Gidley, J.L. and Schechter, R.S.: AcidizingFundamentals, Monograph Series, Vol. 6, SPE, 1979, pp. 99-100.

7. Saudi Aramco Internal Reports.

8. Cinco-Ley, H. and Samaniego, V.F.: “Transient PressureAnalysis: Finite Conductivity Fracture Case vs. DamagedFracture Case,” SPE paper 10179, presented at the SPEAnnual Technical Conference and Exhibition, San Antonio,Texas, October 4-7, 1981.

9. Agarwal, R.G., Gardner, D.C., Kleinsteiber, S.W. andFussell, D.D.: “Analyzing Well Production Data UsingCombined Type Curve and Decline Curve AnalysisConcepts,” SPE paper 49222, presented at the SPE AnnualTechnical Conference and Exhibition, New Orleans,Louisiana, September 27-30, 1998.




Adnan A. Al-Kanaan is the GeneralSupervisor for the Gas ReservoirManagement Division, where he heads ateam of more than 30 petroleumengineering professionals working tomeet the Kingdom’s increasing gasdemand for its internal consumption.

He started his career at the Saudi Shell PetrochemicalCompany as a Senior Process Engineer. Adnan then joinedSaudi Aramco in 1997 and was an integral part of thetechnical team responsible for the on-time initiation of thetwo major Hawiyah and Haradh Gas plants that currentlyprocess 4 BCF of gas per day. He also manages Karan andWasit, the two giant offshore gas increment projects, withexpected total production capacity of 4.3 BCF of gas per day.

Adnan has 13 years of diversified experience in reservoirmanagement, field development, reserves assessment, gasproduction engineering and mentoring young professionals.His areas of interest include reservoir engineering, well testanalysis, simulation modeling, reservoir characterization,fracturing analysis and reservoir development planning.

Adnan received his B.S. degree in Chemical Engineeringfrom King Fahd University of Petroleum and Minerals(KFUPM), Dhahran, Saudi Arabia.

He is a member of the Society of Petroleum Engineers(SPE).

BIOGRAPHIES

Dr. Zillur Rahim is a PetroleumEngineering Consultant with SaudiAramco’s Gas Reservoir ManagementDivision. His expertise includes wellstimulation design, analysis andoptimization, pressure transient testanalysis, gas field development,

planning, and reservoir management. Prior to joining SaudiAramco, Rahim worked as a Senior Reservoir Engineer withHolditch & Associates, Inc., and later with SchlumbergerReservoir Technologies in College Station, TX, where heused to consult on reservoir engineering, well stimulation,reservoir simulation, and tight gas qualification for nationaland international companies. He has taught petroleumengineering industry courses and has developed analyticaland numerical models to history match and forecastproduction and well testing data, and to simulate 3Dhydraulic fracture propagation, proppant transport, andacid reaction and penetration.

Rahim has authored 50 Society of Petroleum Engineers(SPE) papers and numerous in-house technical documents.He is a member of SPE and a technical editor for theJournal of Petroleum Science and Engineering (JPSE).Rahim is a registered Professional Engineer in the State ofTexas, and a mentor for Saudi Aramco’s TechnologistDevelopment Program (TDP). He is also a technical advisorfor the Production Technology team and an instructor forthe Reservoir Stimulation and Hydraulic Fracturing coursefor the Upstream Professional Development Center (UPDC)of Saudi Aramco.

Rahim received his B.S. degree from the Institut Algeriendu Petrole, Boumerdes, Algeria, and his M.S. and Ph.D.degrees from Texas A&M University, College Station, TX,all in Petroleum Engineering.

Mahbub S. Ahmed is a PetroleumEngineering Specialist with SaudiAramco’s Gas Reservoir ManagementDivision. His expertise includesreservoir management, gas fielddevelopment and reservoir simulation.Prior to joining Saudi Aramco in 2001,

Mahbub worked as a Senior Reservoir Engineer with theOccidental Oil and Gas Company in Bakersfield, CA; as aSenior Consultant with Geoquest in Denver, CO; as a SeniorEngineer with Scientific-Software Intercomp in Denver, CO,and as a Reservoir Engineer for the Algerian National OilCompany (Sonatrach). Mahbub has conducted numerousreservoir simulation and engineering studies of oil and gasfields across the U.S., South America and the Middle East.

He received his B.S. degree in 1982 from the InstitutAlgerian du Petrole, Boumerdes, Algeria, and his M.S.degree in 1988 from the University of Oklahoma, Norman,OK, both in Petroleum Engineering.

Mahbub is a member of the Society of PetroleumEngineers (SPE).


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ABSTRACT

In this article, a multiwell data integration approach forimproved formation evaluation is presented to describe: (1) Mixing between injected fresh water and saline formationwater, (2) Water salinity variation vertically across reservoirintervals, and (3) Injected water movement in the reservoir.Data used included static and dynamic time lapse well data aswell as a cross-well electromagnetic (CWEM) survey. Withthis integrated approach, we can answer critical formationevaluation and reservoir management questions, such as whythe formation tester (FT) water sample salinity can be verydifferent from the produced water salinity, and why a welllocated farthest from the injectors can produce water severalyears earlier than a well located nearest the injectors. Gaininga complete knowledge of water salinity, distribution, bothvertically and areally, and water movement in-situ allowsbetter assessment of reservoir saturation changes with time,thereby improving reservoir management.

INTRODUCTION

Globally, carbonate reservoirs dominate oil and gas reservesand production. This is especially true in the Middle East,such as in Saudi Arabia. Compared to clastics, carbonaterocks tend to be more heterogeneous, causing difficulties ingeological characterization. For reservoirs under water -flooding with different water sources (seawater, aquifer waterand surface water), the formation water in swept reservoirintervals is mixed with the different injection waters andconnate water. The resulting mixed salinity environment is amajor challenge for formation evaluation. Diagnosing thereservoir dynamic performance of heterogeneous carbonatesin a mixed salinity environment requires an approach thatintegrates all relevant data, both static and dynamic, includingdata from not only the target well, but also nearby wells, andany cross-well information, if available.

SINGLE WELL MONITORING

Conventionally, reservoir surveillance is conducted using logs,including pulsed neutron logs (with sigma and/or carbon-oxygen (C/O) logging modes) and resistivity logs (induction

logs for open hole completions and cased hole lateral logs forcased hole completions, such as the cased hole formationresistivity tool). The flow meter production logging tool (PLT)provides physical measurements of cumulative fluid flowingfrom reservoir to wellbore. The advantages and limitations ofeach logging technique have been discussed and summarized1,and a generic reservoir surveillance program was proposed.Reservoir performance monitoring for horizontal wells hasalso been recently discussed2.

The downhole wireline formation tester (FT) is another toolthat provides measurements of reservoir pressure and fluidcharacterization at specific depths. In-depth understanding of thereservoir can be achieved with FT measurements, as has beendoc umented3. But, due to its large tool diameter, the FT is usuallyrun in newly drilled wells while the rig is still on location.

In a recent project on monitoring downhole fluid move -ment, a methodology was established to build a dynamicreservoir model by integrating data from a complete loggingsuite with extra deep resistivity array measurements4. Thismethodology, however, was developed for single well dataintegration only.

CROSS-WELL MONITORING

Even with all the previously described formation evaluationtools and methodologies, reservoir surveillance in a single wellis not sufficient to understand the lateral communicationsamong wells, such as water channeling, due to reservoirheterogeneities. To address this critical reservoir managementissue, a project was initiated to field test cross-well electro-magnetic (CWEM) technology for reservoir monitoringbetween wells5, 6.

THE CWEM TRIAL TEST

The CWEM technology was tested in the west flank of acarbonate field, Fig. 1a, which has been under peripheralwaterflooding (from west to east) to maintain pressure andimprove oil recovery. Figure 1b shows the relative locations ofthe three CWEM test wells. To help interpret the CWEMresults, a complete suite of petrophysical logs was acquiredfrom the three wells. The reservoir thickness in this area isaround 150 ft. Geologically, this Jurassic carbonate reservoir


Integrated Multiwell Formation Evaluationfor Diagnosing Reservoir Dynamics

Authors: Dr. S. Mark Ma, Ali R. Al-Belowi, Mohammed A. Al-Mudhhi, Zaki A. Al-Ali and Dr. Murat M. Zeybek



questions and other difficult questions relating to formationevaluation of heterogeneous carbonate reservoirs in a mixedsalinity environment.

VERTICAL VARIATION OF WATER SALINITY IN AMIXED SALINITY ENVIRONMENT AT WELL P2

Understanding water salinity distribution is important for

reservoir surveillance as well as formation evaluation, because

proper reservoir saturation monitoring using resistivity logs

requires accurate formation water characterization in mixed

salinity environments. This is demonstrated using Well P2.

Well P2 is located in the southern part of the CWEM test

area. The well was drilled and put on production in 1996.

The original and the latest (2007) time lapse open hole log

analyses, along with the FT fluid identification stations, are

shown in Fig. 2. Saturation analysis based on the latest time

lapse resistivity data, with a produced water salinity of around

63 thousand parts per million (kppm), showed that two

intervals (labeled II and III) in the middle and an interval with

low porosities at the bottom (labeled IV) were all wet. A C/O

log was also run to obtain salinity independent saturation

(showed as a yellow trace in the new 2007 open hole log

track), and the results were generally in agreement with the

resistivity based interpretation; however, the existence of any

is divided into several zones (Y1 to Y6), Fig. 1c: Zone Y1, atthe top, has low porosity and permeability. Zone Y2,immediately below Zone Y1, is mostly skeletal ooliticlimestone with scattered vugs and local super permeabilityzones. Below Zone Y2 is Zone Y3, which has a high degree ofdolomite occurrences. With their lower rock quality, ZonesY4 and Y5 are less productive than Zones Y2 and Y3, whileZone Y6 is basically nonproductive low porosity limestone.Rock permeability varies from hundreds of millidarcies (mD)at the top to a few mD at the bottom. The hydrocarboncontained in the reservoir is light oil with an API gravity ofabout 33°.

One challenge in analyzing the acquired data in the threeCWEM test wells was that the formation water salinity deter -mined from the FT samples was much more saline than theproduced water salinity. To ensure this was not caused by FTdata acquisition or laboratory measurement errors, an effortwas made to quality control sampling and measurementprocedures by checking the resistivity sensor readings duringsampling as well as cross comparing with laboratory salinitymeasurements on the FT samples from nearby wells.

Objective of This Study

The main objective of this article is to show that an integratedmultiwell approach is required to answer the previous

Figs. 1a, 1b and 1c. The CWEM test area and the target reservoir.


mobile oil in those zones cannot be estimated from saturation

analysis alone because fluid movability also depends on the

characteristics of reservoir relative permeability. This is

especially true in heterogeneous rocks because of their

complex pore structure.

On the other hand, the FT can help to identify mobile fluids

by physically pumping the fluid from the formation. Downhole

fluid analysis (DFA) at Interval II confirmed that the zone has

high water saturation, with about 20% mobile oil fraction

flow, Fig. 2 middle inset, indicating that the remaining oil

saturation in the swept area is higher than the residual oil.

Although good porosity zones at the upper section were wet

(Interval II, and especially Interval III), lower porosity zones at

the bottom (Interval IV) still contained mobile oil, as identified

with DFA, Fig. 2. It should be noted that oil at the lower

porosity zone of Interval IV could have been missed without

the FT DFA, as reservoir saturation monitoring logs (resistivity

or C/O) indicated the section was wet, mainly due to shoulder

bed effect and insufficient vertical resolution. The FT pretests could not yield pressure gradients in this

well due to supercharging. To run the CWEM test, the wellhad needed to be deepened. In the deepening process, a high-pressure source rock was penetrated and the drilling wascompleted with about 1,000 psi overbalance for well control.This overbalance caused severe near wellbore supercharging,which limited the FT pretests.

The salinity of the formation water, determined from the FTresistivity measurement downhole during sampling, was around110 kppm (NaCl eq.). Lab analysis of the FT sample confirmedthat the formation water salinity, i.e., total dissolved solids (TDS)in Interval II (TDSII), was about 130 kppm. Well productionhistory showed that this well became wet in 1998, with waterbreakthrough in Interval III, as shown by the 2001 productionlog. The produced water salinity from water samples taken in2007 (TDSSurf) was about 63 kppm TDS. The latest open holesaturation analysis confirmed that Interval III was all wet andthat Interval II was also wet, but with lower water saturation,


Fig. 2. The original and latest time lapse open hole logs, along with formation testing results in Well P2.



where Q is water flow rate and subscript Surf representssurface conditions, T is total water rate, and III indicates ratesfrom Interval III.

Based on an earlier study7, the average Arabian Gulf watersalinity is about 60 kppm, Fig. 3a. Since the produced watersalinity in Well P2 in 2007 was so close to that of the injected

Fig. 2. This is consistent with the production log results. Using material balance, water salinity in Interval III can be

estimated with the following equation:

(1)

Fig. 3a. Water salinity measured on samples taken before the multiwell survey from different areas of the Arabian Gulf (after Appendix 27).Fig. 3b. Relationship between water flow rates and water salinity based on material balance, Eqn. 1.

Fig. 4. Open hole and cased hole log analysis for Wells P2, P1 and P4.


seawater, from Eqn. 1 and Fig. 3b, it is evident that theproduced water was mainly from Interval III.

WATERFLOOD FRONT MOVEMENT

Diagnosing Waterflood Front Movement from MultiwellAnalysis

Well P1 is located to the east of Well P2, Fig. 1a. The well wasput on production in 1996 and became wet a year later –more than one year earlier than Well P2, which is closer to theinjectors. This indicates that the injected water channeled toWell P1 first, before reaching Well P2, Fig. 4. In 2007, theproduced water salinities from both wells were similar; i.e.,more than 10 years after water broke through the producedwater was a mixture dominated by the injected seawater witha small fraction of connate water.

Well P4 is a well specifically drilled for the CWEM testingproject in 2007, Fig. 1b. Open hole logs identified only one wetzone (Interval II, Fig. 4) and an oil column in the upper goodporosity zones (Interval I as shown in the last track of Well P4in Fig. 4). The FT pressure data yielded clear oil and watergradients (the first track of Well P4 in Fig. 4). The FT pump-out and sampling indicated that the oil column in Interval I wasdry, while DFA and sampling showed that the wet zone ofInterval II was completely swept with no detectable mobile oil,i.e., the reservoir saturation was close to residual oil. The waterin the wet zone of Interval II had a salinity of 120 kppm.Similar to Interval IV in Well P2, Interval III in Well P4contained dry oil, as indicated from DFA and sampling.

Well P3 is located northwest of the CWEM testing area. Itwas put on production in 1996 and became wet in 2001

(3 years after Well P2). Figure 5 shows the historical petro -physical data. Water salinity of the FT samples was 120kppm. A production log survey run in 2003 showed thatinjected water had reached the top of the reservoir. The latestopen hole survey conducted during the CWEM test with slimhole induction array resistivity using formation water salinityaround 120 kppm, showed that all zones were wet, consistentwith the production log result. It should be noted that due toformation dipping, the formation top of this well was around20 ft lower compared to Wells P4 and P2.

After the integration of all relevant data, it was clear that


Fig. 6. Cross-sectional view of the three wells from west to east, in the pilot area.

Fig. 5. Time lapse petrophysical data for Well P3.



second light blue stripe above the first one (labeled as X2 inFig. 7) at Well P2 has higher oil saturation, and the FTconfirmed that the oil was mobile. Saturation at the bottomsection (low porosity zones) is shown with a dark blue color,indicating sweep, although some thin zones, beyond thevertical resolution of the logs, can still produce oil asevidenced at Wells P2 and P4.

SUMMARY

As previously discussed, the integration of multiwell static anddynamic data, along with CWEM results, revealed a clearpicture of how injected fresh Arabian Gulf water mixed withthe more saline reservoir connate water, and how the mixedwater was distributed vertically across the reservoir at locationssuch as Well P2. The multiwell data integration approach alsoenables us to better understand in-situ water movement, such asacross a bottom high porosity and high permeability zone.Water broke through at Well P1 (farthest from the injectors)first, then progressively moved to the northern part of the areauntil four years later, it broke through at Well P3 (nearest to theinjectors), as summarized in Table 1 and illustrated in Fig. 8.This explains why the salinity from the FT water sample can bevery different from the produced water salinity.

CONCLUSIONS

In this article we presented a multiwell data integration approachto formation evaluation to better understand the following:

• Mixing between injected fresh water and salineformation water.

• Water salinity variation vertically across the reservoirintervals.

although Wells P4 and P3 are located closer to the injectorsthan Well P1, Fig. 1, the salinity of the formation water atWell P4 and P3 was much more saline than the water salinityof Wells P1 or P2. Figure 6 shows the open hole formationanalysis logs in a cross section display of the three CWEM testwells, from west to east. By integrating all logs in each welland cross comparing among the wells, the following wasobserved:

• The order of the injected water breakthrough is first atWell P1, then Well P2, and finally at Well P3, i.e., waterbroke through first in the southern part of the area,then progressively moved to the northern part, eventhough Well P3 is much closer to the injectors than Well P1.

• Water first broke through at the bottom of the goodporosity interval. The formation water salinity at thebottom of Interval III at Well P2 was estimated to beclose to the injected water salinity, while the upperswept Interval II contained much higher salinity water.

Diagnosing Waterflood Front Movement from CWEM Data

From the CWEM project, resistivity and saturation maps weregenerated, Fig. 75, 6, with anhydrite at the top and limestonewith almost zero porosity at the bottom, both with very highresistivities (red), which created an almost ideal situation forthe CWEM measurement. High resistivity in the middle(around Well P4) is attributed to the presence of dolomi-tization rather than the existence of hydrocarbons, demonstratingthe importance of using well data for CWEM calibration.

Saturation distribution indicated that the bottom of thegood porosity zone (light blue stripe in the middle) was wetacross the three wells, labeled as X1 in Fig. 7. This isconsistent with saturation and FT results generated at the welllocations. Determination of any remaining mobile oil wouldrequire an integrated approach. In fact, it can be seen that the

Fig. 7. CWEM survey results: Resistivity map at the top and saturation map at thebottom with open hole and FT data.

Fig. 8. Water broke through at Well P1 (farthest from the injectors) first, thenprogressively moved to the northern part of the area until four years later, it brokethrough at Well P3 (nearest to the injectors).

Table 1. Water breakthrough time reference to multiwell survey vs. wells’ relativedistance to injectors

Well P1 P2 P3Water Breakthrough Time 1997 1998 2001Relative Distance to Injectors Farthest Nearest


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• Injected water movement in the reservoir.

Using this approach, we answered the following questions:

• Why the salinity of the FT water sample is so differentfrom the produced water salinity.

• Why a well located farthest from the injectors hadwater breakthrough four years earlier than a welllocated nearest to the injectors.

Gaining a complete knowledge of water salinity distri -bution, both vertically and areally, and water movementin-situ allows better assessment of reservoir saturation changeswith time, thereby providing enhanced reservoir management.

ACKNOWLEDGMENTS

The authors would like to thank the management of SaudiAramco and Schlumberger for permission to publish thisarticle. We also wish to acknowledge the contributions of ourcolleagues, the CWEM team at Saudi Aramco andSchlumberger.

This article was prepared for presentation at the SPE AsiaPacific Oil and Gas Conference and Exhibition, Brisbane,Queensland, Australia, October 18-20, 2010.

REFERENCES

1. Ma, S.M., Al-Hajari, A.A., Berberian, G. andRammamorthy, R.: “Cased Hole Reservoir SaturationMonitoring in Mixed Salinity Environments: A NewIntegrated Approach,” SPE paper 92426, presented at theSPE Middle East Oil and Gas Show and Conference,Manama, Bahrain, March 12-15, 2005.

2. Al-Muthana, A., Ma, S.M., Zeybek, M. and Malik, S.:“Comprehensive Reservoir Characterization withMultiphase Production Logging,” SPE paper 100813,presented at the SPE Saudi Arabia Section TechnicalSymposium, al-Khobar, Saudi Arabia, May 10-12, 2008.

3. Pham, T.R., Al-Afaleg, N.I., Kelder, O., Al-Otaibi, U.F. andZeybek, M.: “Field Example of Capillary Pressure Effectson Wireline Formation Tester Measurements and OWCEstimation in a Mixed Wettability Oil Reservoir,” SPEpaper 93262, presented at the SPE Middle East Oil andGas Show and Conference, Manama, Bahrain, March 12-15, 2005.

4. Zhan, L., Kuchuk, F., Ma, S.M., et al.: “Characterizationof Reservoir Heterogeneity Through Fluid MovementMonitoring with Deep Electromagnetics and PressureMeasurements,” SPE paper 116328, presented at the SPEAnnual Technical Conference and Exhibition, Denver,Colorado, September 21-24, 2008.

5. Marsala, A.F., Ruwaili, S., Ma, S.M., et al.: “Cross-wellElectromagnetic Tomography in Haradh Field: Modeling toMeasurements,” SPE paper 110528, presented at the SPEAnnual Technical Conference and Exhibition, Anaheim,California, November 11-14, 2007.

6. Marsala, A.F., Ruwaili, S., Ma, S.M., et al.: “Cross-wellElectromagnetic Tomography: From Resistivity Mapping toInter-well Fluid Distribution,” IPTC paper 12229,presented at the International Petroleum TechnologyConference, Kuala Lumpur, Malaysia, December 3-5,2008.

7. Jenden, P.D., Al-Dubaisi, J.M., Al-Harthi, O.S., Ma, S.M.and Lyngra, S.: “Chemical and Stable Isotope Models forMonitoring Arab-D Injection Water at Abqaiq andGhawar,” Saudi Aramco Report, February 2008.




Mohammed A. Al-Mudhhi is a SeniorEngineer with the Southern AreaPetrophysics Unit of the ReservoirDescription Division. He joined SaudiAramco in 1978 and has 32 years ofexperience in various petroleumdisciplines, including production

engineering, reservoir management and reservoirdescription.

In 1987, Mohammed received his B.S. degree inPetroleum Engineering from Tulsa University, Tulsa, OK.

Zaki A. Al-Ali is a Senior PetroleumEngineer with the Southern Area’s‘Udhailiyah Reservoir ManagementDivision. Prior to joining Saudi Aramcoin 1987, he worked for the Ministry ofPetroleum and Minerals for 4 years.

Zaki received both his B.S. and M.S.degrees in Petroleum Engineering from King FahdUniversity of Petroleum and Minerals (KFUPM), Dhahran,Saudi Arabia.

Dr. Murat M. Zeybek is aSchlumberger Reservoir EngineeringAdvisor and Reservoir &ProductionDomain Champion in Saudi Arabia,including Bahrain, Kuwait and theNeutral Zone. He works onanalysis/interpretation of wireline

formation testers, pressure transient analysis, numericalmodeling of fluid flow, water control, production loggingand reservoir monitoring.

Murat is a member of the Technical Editorial ReviewCommittee for SPE Reservoir Evaluation & Engineering(SPERE&E). He also served as a committee member forthe 1999-2001 SPE Annual Technical Conference andExhibition. Murat has been a discussion leader and sessionchair in a number of SPE ATW’s, including a TechnicalCommittee member for the SPE Saudi TechnicalSymposium.

He received his B.S. degree from the Technical Universityof Istanbul, Istanbul, Turkey, and his M.S. degree in 1985and Ph.D. degree in 1991 from the University of SouthernCalifornia, Los Angeles, CA, all in Petroleum Engineering.

BIOGRAPHIES

Dr. S. Mark Ma is a PetrophysicsConsultant at the Reservoir Description& Simulation Department (RDSD); onassignment to the UpstreamProfessional Development Center as thePetrophysics Professional DevelopmentAdvisor. A mentor of the Petroleum

Engineering (PE) Advanced Degree Program, Mark alsoserves as a mentor in the PE Technologist DevelopmentProgram and represents Petrophysics in its Technical ReviewCommittee. He is the RDSD Coordinator for PatentApplications.

Before joining Saudi Aramco in 2000, Mark taught atChangjiang University. He also worked as a Lab Scientist atthe New Mexico Petroleum Recovery Research Center, theWyoming Western Research Institute and Exxon ProductionResearch Company.

He received his B.S. degree from China PetroleumUniversity, and his M.S. and Ph.D. degrees from NewMexico Institute of Mining and Technology, Socorro, NM,all in Petroleum Engineering.

Mark is a member of the Society of Core Analysts andthe Society of Petroleum Engineers (SPE), and he serves onthe SPE’s Formation Evaluation Award Committee. He hasover 50 publications in petrophysics and is a technicalreviewer for SPE Reservoir Evaluation & Engineering(SPERE&E) and the Journal of Petroleum Science &Engineering (JPS&E).

Ali R. Al-Belowi the Supervisor for theSouth Area Petrophysics Unit in theReservoir Description and SimulationDepartment. He is responsible forensuring that open hole and cased holelogging programs are optimized,quality log data are acquired, and logs

are analyzed on time utilizing fit-for-purpose petrophysicsmethods. Ali has done extensive work on petrophysicalanalysis in both exploration and development fields.

He joined Saudi Aramco in August 1989 as a PetroleumEngineer after receiving his B.S. degree in PetroleumEngineering from King Saud University, Riyadh, SaudiArabia.


ABSTRACT

Crude oil pipelines are subject to microbiologically influencedcorrosion (MIC), particularly in water pockets at low-lyingsections of the pipeline. Unfortunately, no methods arecurrently effective to monitor crude oil pipelines for thepresence of MIC, and the potential for MIC in individualpipelines remains largely unknown. An effective MICassessment and management strategy will greatly improve thecontrol of internal corrosion of pipelines. This article discussesthe research being conducted in Saudi Aramco’s Research andDevelopment Center (R&DC) to develop MIC assessmentprotocols that will introduce molecular microbiology methods(MMM) to confirm if MIC is the cause of corrosion in aspecific case and procedures that will allow reliable fieldmonitoring of MIC. In the initial stages of the project, severalwater and solid samples were collected from various locationsin a single crude oil pipeline. The samples were analyzed andevaluated to:

1. Determine how many and what types of micro -organisms are present in the samples through theapplication of MMM.

2. Determine the chemical nature of the water in thesystem through geochemical analysis.

3. Investigate the presence of bacteria in solid and filtersamples by using environmental scanning electronmicroscopy and energy dispersive X-ray spectroscopy(ESEM/EDS).

The results of these analyses provided necessary back -ground information for the further implementation of MMMfor MIC risk assessment in the Saudi Aramco crude oilpipeline system.

INTRODUCTION

Corrosion resulting from the attachment and activities ofmicroorganisms on metal surfaces is referred to as microbio-logically influenced corrosion (MIC) or biocorrosion. It occurs in diverse environments and is not limited to aqueoussubmerged conditions, but also takes place in humidatmospheres. It is an electrochemical process in which theparticipation of microorganisms is able to initiate, facilitate, or

accelerate the corrosion reactions without changing theprocess’s electrochemical nature1. MIC is a result of interactionsthat are often synergistic between the metal surface, abioticcorrosion products, and microbial cells and their metabolites.The latter includes organic and inorganic acids and volatilecompounds, such as ammonia and hydrogen sulfide (H2S).Microbiologically mediated reactions do not result in a uniquetype of corrosion, but they can induce localized corrosion,change the rate of corrosion and also inhibit corrosion2.

Most MIC studies have focused on bacterial involvement;however, under aerobic conditions, other single-celledorganisms, such as fungi, yeast and diatoms, can influencecorrosion3. The predominant types of bacteria associated withMIC are sulfate-reducing bacteria (SRB), sulfur-oxidizingbacteria, iron-oxidizing/reducing bacteria, manganese-oxidizing bacteria, and bacteria secreting organic acids andslime4. These organisms coexist within a biofilm matrix onmetal surfaces, functioning as a consortium in a complex andcoordinated manner. The various mechanisms of biocorrosionreflect the variety of physiological activities carried out bythese different types of microorganisms when they coexist inbiofilms. Despite decades of study on MIC, it is still notknown with certainty how many species of microorganismscontribute to corrosion, and researchers continue to report onthe formation of biofilms by an ever-widening range ofmicrobial species.

Historically, the high diversity of the microorganisms andmechanisms that may be involved in MIC has made it very hardto predict and assess the process before substantial damage hasalready been done to the system. Recent technological advancesin the field of molecular microbiology have now made itpossible to detect and enumerate specific MIC promotingmicroorganisms with a much better precision than before.Therefore, given proper procedures for sampling and analyzingfield material, it is now possible to perform a reliable screeningof industrial systems for potentially harmful microorganismsand to use this information in a MIC risk analysis.

The R&DC, in collaboration with the Danish TechnologicalInstitute (DTI), launched a joint research project in early 2010to introduce molecular microbiology methods (MMM) as atool for failure analysis and MIC risk assessment in SaudiAramco’s crude oil pipeline system. In this article, the initial


Microbiologically Influenced Corrosion (MIC)Assessment in Crude Oil Pipelines

Authors: Mazen A. Al-Saleh, Tawfiq M. Al-Ibrahim, Thomas Lundgaard, Dr. Peter F. Sanders, Dr. Ketil B. Sørensen and Dr. Susanne Juhler



findings of the project are presented and discussed, and theway forward is outlined.

Sets of water and solid samples (after scraping) werecollected from locations across the targeted crude oil pipelinesystem, analyzed and evaluated with respect to microbio -logical and chemical composition. A general microbiologicalpopulation study using several molecular microbiologymethods was performed to determine how many and whattypes of microorganisms were present in the system. Thismicrobiological characterization will provide the necessarybasis for the development of customized laboratory kits andprotocols for the detection and enumeration of MIC-relatedmicroorganisms in the Saudi Aramco crude oil pipelinesystem. Knowledge of the chemical composition of the waterphase in the pipeline will be valuable for the development ofprocedures to extract microorganisms and DNA from crudeoil.

EXPERIMENTAL PROCEDURE

Samples Collection and Analysis Profile

Water samples were collected from a vertical outlet in thecrude oil pipeline system to determine the basic waterchemistry and microbiology in the system, Table 1. SamplesSA-1, SA-3 and SA-4 were filtered immediately aftersampling, and the filters were stored in a fixation buffer forlater microbiological analysis. Samples SA-2, SA-5 and SA-6 were used for a chemical analysis of the water. SampleSA-6 was collected in an evacuated container containing a20% zinc-acetate solution to preserve any reduced sulfur inthe water. A solid (debris) sample (SA-7) was collected fromthe scraper trap receiver area during scraping operations ofcrude pipelines and immediately preserved. This samplewas analyzed in the laboratory using environmentalscanning electron microscopy and energy dispersive X-rayspectroscopy (ESEM/EDS) to investigate the presence ofbacteria and the chemical composition, including corrosionproducts, in the solid samples.

Chemical Analysis

The water samples from the identified crude oil pipelines werecharacterized with respect to their organic matter content,salinity, H2S concentration and inorganic elementalcomposition. Part of each sample was filtrated, and theamount of total suspended solids (TSS) was determined. Thesecond part was dried at 550 °C, and the organic content ofthe precipitate was determined by loss of ignition. After dryingand homogenization, the inorganic elemental composition ofthe solids was analyzed using wavelength dispersive X-rayfluorescence (WDXRF) spectroscopy. The concentration ofdissolved H2S in the water was determined by spectrometry.

General Microbial Population Studies

The water samples were analyzed for total microbiological cellnumbers and characterized with respect to the microbiologicalcommunity composition. The total microbiological cellnumbers were determined by Quantitative Polymerase ChainReaction (qPCR) analysis of the 16S rRNA gene content inthe sample. This method uses amplification of genetic materialextracted from samples as a basis for the quantification ofmicroorganisms. Results are given as number of genes(“genetic units” or “GU”) per ml sample, which correspondto the number of microorganisms within a factor of 2 to 3(some groups of microorganisms have more than one 16SrRNA gene copy, and therefore the number of genes is usuallya bit higher than the number of microorganisms). The totalnumber of bacteria and the total number of Archaea werequantified separately, and the sum is the total of theprokaryotic numbers.

The microbiological community composition wasdetermined through cloning, sequencing, and phylogeneticanalysis of specific target genes. To characterize the bacteriaand Archaea present in the water samples, 16S rRNA geneswere amplified using bacteria and Archaea specific primers,respectively. Sulfate reducers were studied by amplifying andcharacterizing genes for the A and B subunits of dissimilatorysulfite reductase (dsrAB).

Table 1. Overview of samples and the performed analyses(1)Quantitative Polymerase Chain Reaction, (2)Cloning and sequencing, (3)Total suspended solids, (4)XRF, (5)ESEM/EDS.

Sample ID Sample Type Chemical Analyses Microbiological Analyses Microscopic AnalysesSA-1 50 ml water, filtered and fixed - qPCR(1), Cloning/Seq(2) -SA-2 200 ml water, unfixed Salinity, TSS(3), XRF(4) - -SA-3 200 ml water, - qPCR(1), Cloning/Seq(2) -

filtered and fixedSA-4 200 ml water, - qPCR(1), Cloning/Seq(2) -

filtered and fixedSA-5 200 ml water, unfixed Organic fraction, - -

Salinity, XRF(4)

SA-6 5 ml water, fixed H2S - -with 1 ml ZnAcetate

SA-7 Solids ESEM/EDS(5)


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ESEM/EDS Analysis

The solid samples were analyzed using a high resolutionESEM integrated with EDS.

The ESEM was operated at 15 kV, 0.23 torr to 0.7 torrwater vapor pressure, and a working distance of about 8 mm.Backscattered electron images together with EDS X-rayspectra were acquired from different parts of the samples. Thesamples were mounted on the ESEM sample holder usingdouble-sided carbon tape.

RESULTS AND DISCUSSION

Chemical Characterization

Results of the chemical measurements are listed in Tables 2, 3and 4. In summary, the salinity in the water was 14.5%, andthe main salts were CaCl2 and MgCl2. A small amount ofsulfide was detected (5.4 mg/L). TSSs amounted to 120 mg/Land consisted mainly of BaSO4, SrSO4, organic material andFe containing corrosion products.

organisms, such as sulfate reducers and methane producers,may be quantified by qPCR in a manner similar to the totalbacterial and Archaeal populations in Table 5; however, to dothis reliably, it is necessary to determine which micro -organisms are present in the system and optimize the qPCRassay based on that5. Therefore, the microorganisms in thesystem were characterized further, as described in Tables 6, 7and 8.

Identification of Microorganisms

Nine different types of bacteria were detected, Table 6. Someof these clusters were highly similar to microorganisms thathave been cultured and are well characterized, includingseveral types of thermophilic and mesophilic bacteria, whichhave been shown to live from SO4

2-, NO3- or Fe3+ reduction

and/or by fermentation of organic material. Other clusters(Unknown Bacterial Clusters I, II and III in Table 6) repre -sented completely unknown groups of bacteria that havenever been cultured or characterized before. Some of theseclusters show similarity to DNA sequences found inthermophilic environments, but their function is completelyunknown. These unknown clusters made up a very largefraction of the clone library (75 out of 110 analyzedsequences, corresponding to 68%). Though it is still unknownwhether they are growing and active in the pipeline system orrepresent dormant organisms originating from the oilreservoir, their function may be highly relevant and potentiallyrelated to MIC in the system.

A total of seven different types of Archaea were detected,Table 7. Three of these clusters (Unknown Archaeal Clusters I,II and III) showed no similarity to previously characterizedorganisms. In total, these three exotic Archaeal groupscomprised 51 of 166 sequences, equal to 31%. As with theunknown bacterial clusters, the function of the unknownArchaea remains enigmatic.

A large fraction of the remaining sequences in the Archaeal

clone library was related to methane producing micro -

organisms (109 of 166 sequences, or 66%). One of the

methanogen clusters detected was related to the genus

Methanothermococcus, which has often been found in scale

material close to corrosion points in production pipeline

systems1. This organism has also been shown to accelerate

corrosion of metal test coupons in laboratory studies6. Several

lines of evidence therefore indicate that Methanothermococcus

is involved in MIC. The other two clusters of methanogens

detected in the water sample carry out the same processes as


Table 2. Salinity, sulfide content and total suspended solids in water samples

Salinity (%) H2S (mg/L) TSS (mg/L)14.5 5.4 120

Table 5. Number of prokaryotic cells in water samples, determined by quantitativepolymerase chain reaction

Sample ID Bacteria (cells/ml) Archaea (cells/ml)SA-1 6.5 x 105 1.15 x 106

SA-3 4.4 x 104 3.0 x 105

SA-4 1.6 x 106 7.0 x 105

Table 3. Elemental composition of dissolved inorganic ions in water samples (%of dry weight)1

1Analysis performed on precipitate after water evaporation.

S Ca Fe Na Mg Cl3.7 9.5 13 0.6 1.6 27

Table 4. Inorganic elemental composition of suspended solids (% of dry weight)1

1Analysis performed on filtrate after drying of filter.

Si S Ca Fe Sr Ba OrganicMaterial

0.8 5.6 0.2 3.6 1.6 8.8 43

Quantification of Microorganisms

The total number of microorganisms in the water from thecrude oil pipeline bypass was determined in triplicate samples,indicating that both Archaeal and bacterial numbers were inthe range of 105 to 106 cells per ml-1, Table 5. It is not yetknown how the numbers of microorganisms in these watersamples are related to the numbers of microorganisms in thecrude flowing in the pipelines. This will be revealed once theprocedures for extracting cell material and DNA from crudesamples have been developed.

The above numbers indicate the size of the total microbialpopulations and thereby give an idea about the overallmicrobiological status of the system. Subsequently, a muchbetter MIC risk analysis can be performed if the number ofspecific MIC causing microorganisms rather than total cellnumbers are known. Troublesome MIC causing micro -


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Table 6. Bacteria phylotypes found in water samples

Taxonomic Group Frequency Physiological Characteristics Based on Closely Related Cultures in Sequence Database

Deferribacteres 1/110 Thermophilic or mesophilic. Probably NO3- or Fe3+ -reducer and/or fermenting organic material.

Synergistes 1/10 Thermophilic or mesophilic.Fermenting organic material.

Firmicutes 25/110 ThermophilicSO4

2- -reducing or fermenting organic material.Thermotoga 6/110 Thermophilic

Actinomycetes 1/110 Fermenting organic material.Chloroflexi 1/110 Thermophilic or mesophilic.

Degrading aromatic and/or chlorinated compounds.Unknown Bacterial 3/110 There are no similar DNA sequences in the database, and this group

Cluster I of microorganisms has apparently never been found before. The function of the group in the environment is therefore unknown.

Unknown Bacterial 1/110 Similar DNA sequences have been found before in thermophilic Cluster II environments, but the microorganisms have never been cultured. We

can conclude that the group is widespread in hot environments, but its function is unknown.

Unknown Bacterial 71/110 This was the most abundant group of microorganisms in the clone Cluster III library. As was the case with UC II, no closely related microorganisms

have ever been cultured, but similar DNA sequences have been found in several studies of thermophilic environments. The group thereforeconsists of thermophilic microorganisms whose metabolism and function is unknown.

Table 7. Archaeal phylotypes found in water samples

Taxonomic Group Frequency Physiological Characteristics Based on Closely Related Cultures in Sequence Database

Methanosaeta 73/166 Mesophilic or thermophilic, with growth at temperatures up to 70 °C. Methanogen that produces methane from acetate.

Methermicoccus 34/166 Thermophilic, with growth at temperatures up to about 70 °C. Methylotrophic methanogen that produces methane from methanol and methylamines.

Thermococcus 6/166 Hyperthermophilic with growth at temperatures up to 100 °C.Grows by fermentation of complex organic material, such as hydrocarbons, polymers, peptides.

Methanothermococcus 2/166 Thermophilic or hyperthermophilic with growth between 17 °C and 90 °C.Produces methane from CO2 and hydrogen. Globally widespread in oil production systems where it may promote MIC.

Unknown Bacterial 2/166 There are no close relatives of this group in culture or in the database. Cluster I The group has apparently never been found before, and its function in

the environment is therefore unknown.Unknown Bacterial 25/166 There are no close relatives in culture, but similar DNA sequences have

Cluster II been retrieved from subsurface marine sediments and from hydrocarbon-rich deep-sea locations.The function of the group in the environment is unknown.

Unknown Bacterial 24/166 There are no close relatives in culture, but similar sequences have been Cluster III found in oil-contaminated soils and hydrocarbon-rich marine sediments.

The function of the group in the environment is unknown.


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Methanothermococcus, but their involvement in corrosive

processes is less well documented. Biocides employed by the

oil and gas industry have in general not been tested for

efficiency against methanogens or other Archaea. The findings

reported here suggest that future biocide test programs should

include methanogens.

The dsrAB genes in the samples were sequenced to

characterize the sulfate-reducing microorganisms in more

detail. This analysis showed that four different types of SRB

were present, Table 8. The detected SRB are all known from

other systems, and together they utilize a diverse selection of

organic substrates as well as hydrogen for the production of

sulfide. No sulfate-reducing Archaea (SRA) was found. The

presence of SRB in the system correlated well with the

detection of sulfide in the water samples and confirmed that

SRB are growing and active in the system.

ESEM/EDS Investigation

The ESEM/EDS results showed that the main elements in the

samples were C, O, Fe and S, with small amounts of Si, Ca, Cl,

P and Na present, Fig. 1. The results also clearly indicated the

presence of microorganisms. This suggests that the presence of

bacteria has contributed to the corrosion of the pipelines,

which is evident in the presence of Fe in the analysis.

Towards an MMM Based MIC Risk Assessment

The goal is to use the MMM based enumeration of MIC

causing microorganisms to provide a sensitive tool for early

warning of MIC events, for general system surveillance and

for failure analysis in Saudi Aramco’s crude oil pipeline

system. To achieve this goal, several tasks still must be

completed:

• Development of protocols for handling of crude

samples. The analysis of crude oil presents some unique

challenges compared to the analysis of water. For

example, it is not trivial to extract microbial cells from

the oil matrix, and various oil components may be

detrimental to the downstream analysis if samples are

not properly purified.

• Analytical protocols must be optimized. There is no

universal assay for specific enumeration of troublesome

microorganisms, such as sulfate reducers or methane

producers, from oil systems. Based on the information

previously presented, it is possible to design customized

qPCR assays for both of these groups in Saudi Aramco’s

crude oil pipeline systems.

• Understanding the link between cell numbers and MICrisk. The final critical step in the MMM based MIC riskassessment will be the translation of cell numbers into areliable risk factor. Based on experience from waterproduced from other systems, the total numbers ofprokaryotes detected in sample SA-1 and SA-4 arerelatively high, whereas the number in sample SA-3 isnot. Consequently, for a number of reasons the cellnumbers from oil systems in other regions may not becomparable to those in Saudi Aramco’s crude oilpipeline. A more detailed MIC risk analysis will becomepossible as a more comprehensive dataset is collectedfrom Saudi Aramco’s crude oil pipeline system.

The goal of ongoing work at Saudi Aramco R&DC and DTIis to undertake these tasks based on the results reported here.


Table 8. Sulfate-reducing microorganisms found in water samples

Taxonomic Group Frequency Physiological Characteristics Based on Closely Related Culturesin Sequence Database

Desulfomicrobium 49/58 Thermophilic.thermophilum Utilizes sulfate, H2, ethanol, lactate, a.o. to generate acetate and sulfide.Desulfoglaeba 1/58 Mesophilic.alkanexedens Degrades n-alkane, a.o. complex organic substrates.

Desulfoglaeba sp. 4/58 Degrades complex organic molecules.Thermode- 4/58 Thermophilic.

sulforhabdus sp. Utilizes sulfate, H2, ethanol, lactate, a.o. to generate acetate and sulfide.

Fig. 1. ESEM images at 2 microns and corresponding EDS X-ray spot analysisspectrum.


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3. Prasad, R.: “Assessment and Control of MIC in the OilIndustry in the 20th Century,” paper 00390, presented atthe NACE International CORROSION 2000 Conferenceand Exhibition, Orlando, FL, March 26-31, 2000.

4. Beech, I.B. and Gaylarde, C.C.: “Recent Advances in theStudy of Biocorrosion – An Overview,” Rev. Microbiology,Vol. 30, No. 3, 1999, pp. 177-190.

5. Skovhus, T.L., Sørensen, K.B., Larsen, J., Rasmussen, K.and Jensen, M.: “Rapid Determination of MIC in OilProduction Facilities with a DNA-based Diagnostic Kit,”SPE paper 130744, presented at the SPE InternationalConference on Oil Field Corrosion, Aberdeen, U.K., May24-25, 2010.

6. Daniels, L., Belay, N., Rajagopal, B.S. and Weimer, P.J.:“Bacterial Methanogenesis and Growth from CO2 withElemental Iron as the Sole Source of Electrons,” Science,Vol. 237, 1987, pp. 509-511.

7. Larsen, J., Sørensen, K., Højris, B. and Skovhus, T.L.:“Significance of Troublesome Sulfate-reducing Prokaryotes(SRP) in Oil Field Systems,” paper 09389, presented at theNACE International CORROSION 2009 Conference andExhibition, Atlanta, GA, March 22-26, 2009.

CONCLUSIONS

The results shown here represent the first phase in theimplementation of MMM for a MIC risk assessment of SaudiAramco’s crude oil pipeline. Chemical and microbiologicalanalyses of water condensates from the pipelines indicatedthat microorganisms, many of whom have the potential tocause MIC, are indeed present in numbers comparable toother systems with documented incidences of MIC7. Based onthe genetic characterization performed here, it will be possibleto design optimized assays for MMM based surveillance andtroubleshooting of MIC in the system.

ACKNOWLEDGMENTS

The authors would like to thank the management of SaudiAramco for their support and permission to publish thisarticle. The authors would also like to acknowledge thesupport from the Saudi Aramco Pipelines Department and theAnalytical Support Division of the Saudi Aramco Research &Development Center. Their skills and dedicated efforts inconducting these very challenging laboratory tests are highlyappreciated.

REFERENCES

1. Videla, H.A.: Manual of Biocorrosion, Boca Raton, FL,CRC Lewis Publishers, 1996.

2. Videla, H.A.: “Corrosion Inhibition in the Presence ofMicrobial Corrosion,” paper 223, presented at the NACEInternational CORROSION 1996 Conference andExhibition, Denver, CO, March 24-29, 1996.



Dr. Peter F. Sanders is a ResearchScience Consultant and Water SystemsProject Leader in Saudi Aramco’sResearch & Development Center(R&DC). He worked for 12 years asSenior Microbiologist and ResearchManager at Oil Plus Ltd., an oil field

consultancy company in the U.K., working on solvingmicrobiological problems for most of the major oil fieldoperators all over the world. Prior to that, Peter was aResearch Fellow at Aberdeen University, Scotland, and ran asmall oil field microbiology company for 8 years, servicingthe developing North Sea oil industry.

He joined Saudi Aramco in 2001, and has been workingon developing new chemical solutions to combat microbialcorrosion, biofouling and contamination problems in oilproduction and utilities systems. Peter has recently beenstudying downhole microbial growth, developing novelbiotechnology processes for upgrading crude oil quality in-situ and for enhancing oil production efficiencies, as well asdeveloping resources and competencies in the BiotechnologyGroup of the R&DC.

He received his B.S., M.S. and Ph.D. degrees inMicrobiology from Exeter University, Exeter, U.K.

Dr. Ketil B. Sørensen is the R&DCoordinator at the DanishTechnological Institute (DTI), Oil &Gas Group. He has been with DTIsince 2007, and now focuses mainly ondevelopment and implementation ofadvanced molecular microbiological

tools within the oil and gas industry.Ketil received his M.S. degree in Microbiology from

Aarhus University, Aarhus, Denmark, and then received hisPh.D. degree in Microbiology from the University ofSouthern Denmark, Funen, Denmark, in 2002. He has 5years of post doc experience, working in the U.S. andEurope, generally within the field of molecularmicrobiology.

Dr. Susanne Juhler is a MicrobiologistConsultant at the Danish TechnologicalInstitute (DTI), Oil & Gas Group. Shehas been working with applied micro -biology since 2005, focusing onresearch studies involving molecularmicrobiology methods and electro-

chemical micro-sensors for analysis of microbial communitystructures and activity levels. Since the spring of 2009,Susanne has been occupied with oil field microbiology atDTI, mainly focusing on R&D projects for the oil industry.

She received her Ph.D. degree in Microbiology fromAarhus University, Aarhus, Denmark.

BIOGRAPHIES

Mazen A. Al-Saleh joined SaudiAramco’s Research and DevelopmentCenter (R&DC) in November 2001 asa Lab Scientist. He is now working inthe Biotechnology Group of theResearch & Development Division(R&DD). Mazen joined the

Engineering Service’s Specialist Development Program (SDP)in June 2005, targeted to become one of the company’sspecialists in petroleum microbiology. He is currently theProject Leader for the Microbially Influenced Corrosion(MIC) in Pipelines part of the R&DC Upstream ProgramDirection.

Mazen’s research focus has been in the area of appliedmicrobiology and biocorrosion.

Mazen received his B.S. degree in Chemistry at ToledoUniversity, Toledo, OH, in 1996.

Tawfiq M. Al-Ibrahim has been withSaudi Aramco for almost 30 years. Heis now working as a Science Specialistin the Research and DevelopmentCenter (R&DC) and was one of thefirst five Saudis to join the Center in1983 when its task force was all

expatriates. During his time with Saudi Aramco, Tawfiq hasheld several positions, including Group Leader, ResourceDeveloper, Supervisor, Superintendent and the ESTechnology Program Director.

In 1990, he received his B.S. degree in Chemistry fromthe University of Alabama, Tuscaloosa, AL. Tawfiq iscurrently working on his M.S. degree in ProjectManagement (Oil and Gas) as a self-development processwith the University of Liverpool, Liverpool, U.K.

Thomas Lundgaard is a SectionManager at the Danish TechnologicalInstitute (DTI), Oil & Gas Group. Hestarted as an R&D Consultant in 2005and then moved into his currentposition in 2010. Thomas has a broadbackground in R&D gained from

industry consultancy within applied microbiology andcolloidal chemistry in technical water systems. For the last 5years, he has been focusing on technical operations support,microbiological monitoring programs and R&D projects inthe oil and gas industry.

In 2005, Thomas received his M.S. degree inEnvironmental Engineering from Aalborg University,Aalborg, Denmark.


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ABSTRACT

Boiler tubes have occasionally failed in refineries and gas

plants. The Research and Development Center (R&DC) has

helped plant engineers overcome problems by identifying the

nature and source of compounds leading to failure (e.g.,

corrosion products, formation materials and scale deposits).

The boiler consists of a furnace, boiler tubes, steam drum,

mud drum and boiler. The furnace provides heat to the boiler,

changing water into steam. The presence of certain

compounds in sample deposits from boiler tubes can indicate

why they fail. The presence of vanadium and sodium

compounds in the sample indicates the quality of burning

fuels is poor. If large quantities (>15%) of hematite, Fe2O3,

appear in the deposits, it indicates the presence of dissolved

oxygen in the boiler feed water. If iron carbonate appears in

the samples, it indicates the presence of dissolved carbon

dioxide (CO2) in the system. If the metallic copper is present

in the deposits, it indicates erosion in the boiler. In the last

case, special precautions to prevent the plating out of copper

during cleaning operations are required. The procedures used

to identity corrosion products, formation materials and scale

deposit materials will be described.

INTRODUCTION

The furnace, boiler tubes, steam drum, mud drum and boiler

are all parts of the boiler. When the furnace provides heat to

the boiler, the water changes into steam. The failure of boiler

tubes has been observed in refineries and gas plants for several

years. The failures occurred mainly due to corrosion erosion,

deposition and scale formation. Due to the failure of boiler

tubes, the refinery and gas plants were shut down, ultimately

causing a financial loss. Therefore, the Research and

Development Center (R&DC) provides support to plant

engineers in identifying the nature and source of compounds

leading to failure (e.g., corrosion products, formation materials

and scale deposits), using X-ray powder diffraction (XRD) and

X-ray fluorescence (XRF) techniques. The findings will help

engineers to take proper action to prevent future occurrences,

avoiding plant slowdowns that result in loss of production.

ADVANTAGES OF THE XRD TECHNIQUE

XRD is an excellent analytical technique used for the phaseidentification of a crystalline material in the form of a solid orpowder, such as a catalyst, scale deposit, chemical, core, shell,clay mineral and cement1-10. XRD identifies com pounds,whereas XRF, induced coupled plasma and atomic absorptiontechniques only identify elements. A sample XRD and XRFanalysis is given in Table 1.

The XRD method1-10 differentiates between different formsof a compound with the same chemical formula. If the sampleis calcium carbonate (CaCO3) either it is calcite (scaleformation materials), aragonite (scale), or vaterite. Addi tionally,XRD identifies the forms of a compound, such as the ironsulfides (FeS), that have different chemical formulae; e.g., pyrite(FeS2), marcasite (FeS2), mackinawite (FeS0.9), pyrrhotite (Fe7S8)and greigite (Fe3S4). It is very important to know the form of(FeS), because some of these iron sulfides are pyrophoric.Furthermore, the XRD technique can differentiate thehydration state of compounds, e.g., gypsum (CaSO4.2H2O),bassanite (CaSO4.0.5H2O) and anhydrite (CaSO4).

XRD APPLICATIONS IN SAUDI ARAMCO

The X-ray Group of the Analytical Service Division fullysupports the R&DC Downstream and Strategic and Upstreamresearch projects, on topics such as scale mitigation, oil tohydrogen, pipeline integrity, black powder and catalysts. Wesupport the Engineering Service Agreement projects, includingmineralogical determination for the Qusaiba shale in thenorthwest region (ERAD), analytical services support for theOperation Service Division of the EXPEC ARC, andhydrajetting impacts in the filtration system. We support theTechnical Service Projects (TSPs) in facilities, such as the


Characterization of Corrosion Products inSaudi Aramco’s Oil and Gas Facilities Using the X-ray Powder Diffraction Method

Authors: Dr. Syed Rehan Zaidi, Dr. Husin Sitepu and Ahmed A. Al-Shehry

Table 1. XRD phase identification and XRF elemental analysis of corrosion products

Weight Percentage (Wt%)XRF XRD

Element Wt% Element Wt% Compound Wt%Fe 60.4 Ca 0.2 Magnetite-Fe304 44S 28.8 Si 0.1 Hematite-Fe2O3 28

Na 0.5 P 0.1 Pyrite-FeS2 7Al 0.2 - - Sulfur-S 21


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Rabigh refinery, Jiddah refinery and Ras Tanura refinery(RTR), Berri gas plant (BGP), Ju’aymah gas plant and Abqaiqgas plant, and the Consulting Services Department (CSD).

In the present study, we identified the compounds ofdeposits formed in the boilers of refineries and gas plants byusing the XRD technique, as requested by the TSP ProgramDirector, and determined the nature, source and formationmechanism of the deposits. Once all the phases for each of thedeposits in the XRD data had been identified using HighScorePlus software, then the quantitative phase analysis, i.e., weightpercentage (Wt%) for each phase, was completed using theRietveld method7-9. The findings will help refinery and gasplant employees to take proper action to prevent futureoccurrences.

EXPERIMENTAL PROCEDURE

Sample

The starting powders were collected from boiler tubes F-3001in the Jiddah refinery, the RTR boiler #8 tubes, screen tubesof a boiler at the Yanbu’ gas plant and at boilers at the Berrigas plant. The deposits were considered to be excellentcanidates for this study because these deposit materials causedthe failure of the refineries’ and gas plants’ boiler tubes. Theuntreated deposit samples were manually ground with anagate mortar and a pestle, and homogenized. Subsequently, 5 grams of the homogenized deposit sample were furthermanually ground for several minutes to achieve a fine particlesize. This grinding was conducted to achieve adequateintensity reproducibility7-8. The fine powder was thenmounted into the XRD sample holder by back pressing.

XRF Spectra Measurements

XRF is one of the powerful analytical tools used to determinethe elemental composition of all kinds of materials. In thisstudy, we used the SPECTRO energy dispersive X-rayfluorescence (EDXRF) spectrometry to analyze the elementalcompositions of the corrosion product samples. Thisspectrometry simultaneously detects the element from sodium(Na) to uranium (U). The concentration range goes from thepart per million level to 100%. Due to the X-ray interactionwith electrons, the elements with high atomic numbersprovide better accuracy than the lighter elements.

Four grams of the fine powder were mixed well andhomogenized with 0.9 grams of Licowax C micropowder PMbinder (Hoechstwax). The homogenized mixtures werepressed at a pressure of 20 tons to form pellet samples with adiameter of 31 mm. The pellet samples were then irradiatedwith X-ray photons from a molybdenum X-ray tube. Theenergies of the X-rays emitted by the sample were measuredusing a silicon semiconductor and were processed by amultichannel analyzer. Subsequent processing of the dataprovided spectral information that identified the elementspresent in the sample and the intensities of the

X-rays emitted by these elements. The intensity of the rayswas processed by the instrument’s software to determine theelemental concentrations.

XRD Data Measurements

Step-scanned patterns were measured with a PANalyticalX′PERT XRD diffractometer. The XRD data were measuredfrom 4° to 90° in a 2θ Bragg angle. A position sensitivedetector (PSD) X′Celerator was used with a scanning step timeof 10 seconds, ensuring reasonable intensity countingstatistics. PSD length in 2θ was 2.12°, with irradiated andspecimen lengths of 1 mm and 10 mm, respectively. A detectorstep size of 0.01 was employed to provide adequate samplingof the peaks (full width at the half maximum (FWHM)approximately 0.11° to 0.34°). All of the samples were spunduring the data collection to improve particle countingstatistics, so the intensities are not dominated by a smallnumber of crystallites. Figure 1 shows the goniometer of theXRD instrument used in this study.

XRD Phase Identification

For the qualitative analysis (i.e., phase identification), thesoftware package PANalytical HighScore Plus, combined withthe International Center for Diffraction Data (ICDD) andpowder diffraction file (PDF) database of the standardreference materials, was used. Elemental composition dataobtained from the EDXRF analysis was used as additionalinformation for the phase identification.

Quantitative Phase Analysis (QPA) Using the

Rietveld Method

Quantitative Phase Analysis (QPA) of multicomponentmixtures10-14 using XRD data10 has been used worldwide todetermine the Wt% for a given phase. This QPA method doesnot require measurement of calibration data or the use of aninternal standard; however, knowing the approximate crystalstructure of each phase of interest in a mixture is necessary.The use of an internal standard10 will allow the determinationof total amorphous phase content in a mixture. Analysis ofsynthetic mixtures has yielded high-precision results, with


Fig. 1. The parts of the XRD goniometer and optics.



to take corrective measures, preventing scale buildup andavoiding future tube failure. Table 2 shows the elementalcomposition obtained from the EDXRF spectrometry and thechemical compounds obtained from the XRD technique.

Vanadium, sulfur and sodium are present in fuel oil. Whenfuel oil is burned, vanadium and sodium compounds — presentin the fuel in high quantities — react with oxygen to form V2O5

and Na2O in the furnace, and they stick to the metal surface.The V2O5 and Na2O react on the metal surface to form a lowmelting point phase of the conpound, such as sodium vanadate.Under optimum conditions, they can form a liquid that fluxesthe protective oxide scale, exposing the underlying metal tooxidation. Therefore, these ash deposits pose potentialcorrosion problems.

The presence of sodium oxide and vanadium oxide in ashdeposits suggests the occurrence of fuel ash corrosion. Tomitigate this corrosion, fuel oils that contain low quantitiesof vanadium, sodium and sulfur are used. If the aboveoption is not possible, a fuel additive treatment can beadopted to prevent the formation of a low melting pointphase of the sodium vanadate complexes. Additivescontaining magnesium and aluminum oxide have beensuccessful in controlling fuel ash corrosion.

Identification of Scale Removed from RTR Boiler Tubes

Scale deposits were observed in the high-pressure boilertubes at RTR. Figure 2 shows two samples of the affectedtube sections submitted to the R&DC TSP ProgramDirector: (1) A section cutout from wall and screen, and (2)A large tube. For the tube in Fig. 2a, an analysis wasrequired to identify the deposits. The identification of thedeposits led to a procedure to chemically clean the boilertubes without damaging them.

errors generally less than 1.0% absolute. Since this techniquefits the complete diffraction pattern, it is less susceptible toprimary extinction effects and minor amounts of preferredorientation6-9. Additional benefits of this technique — overtraditional quantitative analysis methods1-5 — include: (1) Thedetermination of precise cell parameters and approximatechemical compositions, and (2) The potential for thecorrection of preferred orientation6-9 and microabsorptioneffects15.

The weight of a phase in a mixture is proportional to theproduct of the scale factor, as derived in a multicomponentRietveld analysis of the powder diffraction pattern, with themass and volume of the unit cell. If all phases are identifiedand crystalline, the weight fraction W of phase p is given by:

where s, Z, M and V are the Rietveld scale factor, the numberof formula units per unit cell, the mass of the formula unitand the unit-cell volume (in Å3), respectively. This equation isthe basis of a method providing accurate phase analyseswithout the need for standards or for laborious experimentalcalibration procedures. It is noted that the Rietveld methodfor quantification of a mixture of 20 phases using theHighScore Plus software (with a total of 6,000 reflections) is30,000 times more powerful than the reference intensity ratiomethod1-5 for the quantification of just two phases of themixture.

RESULTS AND DISCUSSIONS

Analysis of External Deposits from Boiler Tubes in theJiddah Refinery

The Jiddah refinery boiler is an oil-fired boiler. A hugeaccumulation of ash deposit was observed on the externalsurface of the tubes. The engineers asked the R&DC TSPProgram Director to identify the deposits to determine thesource and formation mechanism, so they could use the results

Table 2. Summary of elemental and chemical compositions of the Jiddah boilerdeposits

Weight Percentage (Wt%)XRF Elemental XRD Chemical CompositionComposition

V 31.5 Vanadium oxide - V2O5 70S 5.9 Sodium vanadium oxide - 15

NaV2O5

Ni 5.4 Sodium vanadium sulfate 13hydrate - Na(SO4)2. H2O

Na 4.6 Mackinawite - FeS 2Si 1.3 Calcite - CaCO3 TraceFe 1.2 - -Al 0.9 - -Ca 0.2 - -

Figs. 2a and 2b. Scale deposits accumulated in high-pressure boiler tubes at RTR.

2a 2b

Fig. 3. XRD histogram of deposits from boiler tubes at RTR (inside screen tube)along with the reference patterns of identified phases.


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The XRD results, Table 3 and Figs. 3 to 6, showed that thescale deposits scraped from the insides of the tubes mainlyconsisted of iron oxide corrosion products with calciumphosphate hydroxide (apatite) and magnesium phosphatehydroxide. The deposits removed from the outside of the largetube consisted of calcium sulfate and iron oxide corrosionproducts. Additionally, the high percentage of hematite mightindicate the presence of dissolved oxygen in the boiler water.

Identification of Deposits from Screen Tubes of a Boiler atYanbu’ Gas Plant

Scale deposits were observed in a high-pressure boiler at theYanbu’ gas plant. The boiler tube failed due to these deposits.

The deposits were removed from the boiler tube andsubmitted by the CSD to R&DC to support failure analysiswork. The XRD results, Table 4 and Fig. 7, showed a highpercentage of hematite and metallic copper, which indicatesthe presence of dissolved oxygen in the boiler feed water andalso erosion in the boiler tubes.

Identification of Scale Deposits Removed from BGP

An unknown material — produced with a sulfur product —was found in a condenser at a plant in the BGP sulfurrecovery unit. The plant engineers’ concern was that theunknown material might be from the super claus catalyst(alumina and silica). If this was the case, it meant that themesh holding the catalyst has a pinhole, causing a catalystleak, which would require total plant shutdown and catalystremoval to repair or replace the mesh.

The XRD results, Table 5 and Figs. 8 to 10, showed noalumina or silica, as expected by the BGP engineers. It meant thatthe mesh holding the catalyst is good. Ammonium hydrogensulfate can be formed in the boiler feed water due to treatmentwith the chemical compound, which contains ammonia. Theformation of ammonium hydrogen sulfate can be avoided byincreasing the furnace temperature to burn the ammonia.


Table 3. Summary of chemical compositions of deposits from boiler tubes at RTR

Weight Percentage (Wt%)Screen Side Large Large

Compounds Tube Wall Tube TubeInside Inside Inside Outside

Magnetite-Fe304 41 46 60 47Hematite-Fe2O3 34 28 14 12Hydroxylapatite -Ca5(PO4)3(OH) 13 13 11 -Magnesium phosphate hydroxideMg2(PO4)OH 12 13 15 -Anhydrite - CaSO4 - - - 13Talc - Mg3Si4O10(OH)2 - - - 8

Table 4. Summary of results of deposits from a boiler at Yanbu’ gas plant

Compound Weight Percentage (Wt%)Magnetite - Fe3O4 39Hematite - Fe2O3 35

Copper - Cu 26

Fig. 4. XRD histogram of deposits from boiler tubes at RTR (inside wall tube)along with the reference patterns of identified phases.

Fig. 5. XRD histogram of deposits from boiler tubes at RTR (inside large tube)along with the reference patterns of identified phases.

Fig. 6. XRD histogram of deposits from boiler tubes at RTR (outside large tube)along with the reference patterns of identified phases.

Fig. 7. XRD histogram of deposits from a boiler at Yanbu’ gas plant along withthe reference patterns of identified phases.


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oil is not good; if hematite is present in the boiler deposits, itmeans that the boiler feed water contains dissolved oxygen; and ifthe metallic copper is present in the deposits, it indicates erosionin the boiler tubes. Special precautions must then be taken toprevent the plating out of copper during cleaning operations.

ACKNOWLEDGMENTS

The authors would like to thank the management of SaudiAramco for permission to publish the results in this article.Awad M. Al-Mofleh, Yazeed Al-Dukhayyil and Abdulelah Al-Naser are acknowledged for their encourage ment and supportfor this study. Thanks are also due to Fahad Al-Khaldi for hishelp in preparing the XRF samples.

REFERENCES

1. Chung, F.H.: “Quantitative Interpretation of X-rayDiffraction Patterns of Mixtures I. Matrix-flushing Methodfor Quantitative Multicomponent Analysis,” Journal ofApplied Crystallography, Vol. 7, 1974a, pp. 519-525.

2. Chung, F.H.: “Quantitative Interpretation of X-rayDiffraction Patterns of Mixtures II. Adiabatic Principle ofX-ray Diffraction Analysis of Mixtures,” Journal ofApplied Crystallography, Vol. 7, 1974b, pp. 526-531.

3. Chung, F.H.: “Quantitative Interpretation of X-rayDiffraction Patterns of Mixtures III. SimultaneousDetermination of a Set of Reference Intensities,” Journal ofApplied Crystallography, Vol. 8, 1975, pp. 17-19.

4. Klug, H.P. and Alexander, L.E.: X-Ray DiffractionProcedures for Polycrystalline and Amorphous Materials,2nd edition, New York: John Wiley & Sons Inc., 1974.

5. Jenkins, R. and Snyder, R.L.: Introduction to X-rayPowder Diffractometry, New York: John Wiley & SonsInc., 1996.

6. Sitepu, H., Sherik, A.M., Zaidi, S.R. and Shen, S.:“Comparative Evaluation of Cobalt and Copper Tubesusing X-ray Diffraction Data for Black Powder in SalesGas Transport System,” paper 10100, presented at the 13th

Middle East Corrosion Conference, Manama, Bahrain,February 14-17, 2010.

7. O’Connor, B.H., Li, D.Y. and Sitepu, H.: “Strategies forPreferred Orientation Corrections in X-ray PowderDiffraction Using Line Intensity Ratios,” Advances in X-ray Analysis, Vol. 34, 1991, pp. 409-415.

8. Sitepu, H., O’Connor, B.H. and Li, D.Y.: “ComparativeEvaluation of the March and Generalized SphericalHarmonic Preferred Orientation Models Using X-rayDiffraction Data for Molybdite and Calcite Powders,”Journal of Applied Crystallography, Vol. 38, 2005, pp.158-167.

9. Sitepu, H.: “Texture and Structural Refinement of NeutronDiffraction Data of Molybdite (MoO3) and Calcite

CONCLUSIONS

XRD is an excellent tool to determine the nature, source andformation mechanism of deposits formed by the processes in thevarious units of refineries and gas plants. The XRD results canguide the engineers at the affected refinery and gas plant toovercome the problems by devising the right correctiveprocedures. For example, if sodium and vanadium compoundsappear in the samples (ash deposits) examined, it indicates the fuel

Table 5. Summary of chemical compositions of deposits from BGP

Weight Percentage (Wt%)Plant Plant Plant

Compounds Deposit as White YellowReceived Part Part

Ammonium hydrogensulfate - (NH4)3H(SO4)2 84.8 100 92.2Sulfur - S 15.2 - 7.8

Fig. 8. XRD histogram of the as-received deposits from BGP along with thereference patterns of identified phases.

Fig. 9. XRD histogram of deposits (white part) from BGP along with thereference patterns of identified phases.

Fig. 10. XRD histogram of deposits (yellow part) from BGP along with thereference patterns of identified phases.


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(CaCO3) Powders and Ni50.7Ti49.30 Alloy,” PowderDiffraction Journal, Vol. 24, No. 4, 2009, pp. 315-326.

10. Bish, D.L. and Howard, S.A.: “Quantitative PhaseAnalysis Using the Rietveld Method,” Journal of AppliedCrystallography, Vol. 21, Part 2, April 1988, pp. 86-91.

11. Madsen, I.C., Scarlett, N.V.Y., Cranswick, L.M.D. andLwin, T.: “Outcomes of the International Union ofCrystallography Commission on Powder DiffractionRound Robin on Quantitative Phase Analysis: Samples 1ato 1h,” Journal of Applied Crystallography, Vol. 34,2001, pp. 409-426.

12. Scarlett, N.V.Y., Madsen, J.C., Cranswick, L.M.D., et al.:“Outcomes of the International Union of CrystallographyCommission on Powder Diffraction Round Robin onQuantitative Phase Analysis: Samples 2, 3, 4, SyntheticBauxite, Natural Granodiorite and Pharmaceuticals,”Journal of Applied Crystallography, Vol. 35, 2002, pp. 383-400.

13. Hill, R.J. and Howard, C.J.: “Quantitative Phase Analysisfrom Neutron Powder Diffraction Data Using theRietveld Method,” Journal of Applied Crystallography,Vol. 20, 1987, pp. 467-474.

14. O’Connor, B.H. and Raven, M.D.: “Application of theRietveld Refinement Procedure in Assaying PowderedMixtures,” Powder Diffraction Journal, Vol. 3, No. 4,1988, pp. 2-6.

15. Hermann, H. and Ermrich, M.: “Microabsorption of X-ray Intensity in Randomly Packed Powder Specimens,”Acta Crystallographica Section A, Vol. A43, No. 3, 1987,pp. 401-405.


Dr. Husin Sitepu joined Saudi Aramco’sResearch and Development Center(R&DC), Analytical Services Division,in 2008. Currently, he is contributingto several research projects under boththe Downstream and Strategic andUpstream R&DC programs, in

providing crystallographic information on developedmaterials including nano-materials and catalysts. Beforejoining Saudi Aramco, Husin worked at NIST Center forNeutron Research in Gaithersburg, MD; Virginia TechUniversity in Blacksburg, VA; Ruhr University BochumUniversität in Bochum, Germany; the Institute Laue-Langevin, Neutrons for Science, in Grenoble, France; theUniversity of British Columbia in Vancouver, Canada; andthe Curtin University of Technology in Perth, Australia.

He has authored and coauthored 32 papers in severalpeer-reviewed journals, including the International Union ofCrystallography’s Journal of Applied Crystallography.Husin has extensive experience in Rietveld refinement ofpolycrystalline structures using X-ray, synchrotron andneutron powder diffraction data.

He received his Postgraduate Diploma, M.S. and Ph.D.degrees in Physics from the Curtin University of Technology,Perth, Western Australia, in 1989, 1991 and 1998,respectively.

Husin is a member of the International Center forDiffraction Data (ICDD), the International Union ofCrystallography (IUCr), and the Neutron Scattering Societyof America (NSSA).

Ahmed A. Al-Shehry has worked atSaudi Aramco since 1981. He startedhis career working in the ChemistryAnalysis Unit of the LaboratoryDepartment. He now works in theElemental Analytical Unit, part of theAnalytical Services Division of R&DC.

Ahmed is a Senior Digital System Technician and anexpert in several analytical techniques, including atomicabsorption spectromery (AAS), which is an analyticalprocedure for the qualitative and quantitative determinationof chemical elements employing the absorption of opticalradiation (light) by free atoms in the gaseous state; flameatomic absorption spectrometry, which is a very commontechnique for detecting metals and metalloids inenvironmental samples; inductively coupled plasma opticalemission spectrometry (ICP-OES), which is an analyticaltechnique used for the detection of trace metals; and XRDand XRF used to determine the chemical compositions ofcorrosion products, carbonate rocks, cements and catalysts.

He has successfully conducted a series of Technical ServiceProjects (TSPs) to support refineries and gas plants. Also,Ahmed has contributed to the Downstream and Strategic, andUpstream R&DC programs by providing chemicalcompositions of scale mitigation and catalysts projects.

BIOGRAPHIES

Dr. Syed Rehan Zaidi has been withSaudi Aramco since 1992. Hisspecialized area of research is themineralogical characterization ofgeological samples (clay and bulk rock)by using the XRD technique. Syed isalso responsible for the XRD method

development and research work. He is also familiar with theother analytical techniques, such as: XRF, SEM, FTIR,TGA, DSC and ICP instruments.

Syed received his B.S. (Honors) and M.S. degrees inChemistry from Aligarh Muslim University, Aligarh, India,in 1977 and 1980, respectively. In 1986, he received hisPh.D. degree in Inorganic Chemistry from Aligarh MuslimUniversity, Aligarh, India.

Syed has published more than 20 papers in peer reviewjournals. He is a member of the American Chemical Society(ACS) and the Society of Petroleum Engineers (SPE).



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TO ORDER

By phone/e-mail:

Saudi Aramco Public Relations Department JOT Distribution+966/[email protected]

By fax:

Saudi Aramco Public Relations Department JOT Distribution+966/3-873-6478

By mail:

Saudi Aramco Public Relations Department JOT Distribution Box 5000Dhahran 31311Saudi Arabia

Current issues, select back issues and multiple copies of some issues are available upon request.

The Saudi Aramco Journal of Technology is published by the Saudi Aramco Public Relations Department, Saudi Arabian Oil Company, Dhahran, Saudi Arabia.




GUIDELINES FOR SUBMITTING AN ARTICLE TO THE SAUDI ARAMCO JOURNAL OF TECHNOLOGY

These guidelines are designed to simplify and help standardizesubmissions. They need not be followed rigorously. If youhave additional questions, please feel free to contact us atPublic Relations. Our address, fax and phone numbers arelisted on page 70.

Length

Varies, but an average of 2,500-3,500 words, plusillustrations/photos and captions. Maximum length should be 5,000 words. Articles in excess will be shortened.

What to send

Send text in Microsoft Word 6.0/95 or higher (do not submit UNIX files) via e-mail or on disc, plus one hard copy.Send illustrations/photos and captions separately but concurrently, both as e-mail or as hard copy (moreinformation follows under Format).

Procedure

Notification of acceptance is usually within three weeks. Thearticle will be edited for style and clarity and returned to theauthor for review. All articles are subject to the company’snormal review. No paper can be published without a signatureat the manager level or above.

Format

No single article need include all of the following parts. Thetype of article and subject covered will determine which partsto include.

Working title

Abstract

Usually 100-150 words to summarize the main points.

Introduction

Different from the abstract in that it “sets the stage” for thecontent of the article, rather than telling the reader what it is about.

Main body

May incorporate subtitles, artwork, photos, etc.

Conclusion/summary

Assessment of results or restatement of points in introduction.

Endnotes/references/bibliography

Use only when essential. Use author/date citation method inthe main body. Numbered footnotes or endnotes will beconverted. Include complete publication information.Standard is The Associated Press Stylebook, 39th ed.

Acknowledgments

Use to thank those who helped make the article possible.

Illustrations/tables/photos and explanatory text

Submit these separately. Do not place in the text. Positioningin the text may be indicated with placeholders. Initialsubmission may include copies of originals; however,publication will require the originals. When possible, submitboth electronic versions, printouts and/or slides. Color ispreferable.

File formats

Illustration files with .EPS extensions work best. Otheracceptable extensions are .TIFF, .JPEG and .PICT.

Permission(s) to reprint, if appropriate

Previously published articles are acceptable but can bepublished only with written permission from the copyrightholder.

Author(s)/contributor(s)

Please include a brief biographical statement.

Submission/Acceptance Procedures

Papers are submitted on a competitive basis and are evaluatedby an editorial review board comprised of various departmentmanagers and subject matter experts. Following initialselection, authors whose papers have been accepted forpublication will be notified by e-mail.

Papers submitted for a particular issue but not accepted forthat issue will be carried forward as submissions forsubsequent issues, unless the author specifically requests inwriting that there be no further consideration. Paperspreviously published or presented may be submitted.

Submit articles to:

EditorThe Saudi Aramco Journal of TechnologyRoom 2240 East Administration BuildingDhahran 31311, Saudi ArabiaTel: +966/3-873-5803Fax: +966/3-873-6478E-mail: [email protected]

Submission deadlines

Issue Abstract submission deadline Release date

Fall 2011 June 1, 2011 September 30, 2011Winter 2011 August 31, 2011 December 31, 2011Spring 2012 November 30, 2011 March 31, 2012Summer 2012 February 29, 2012 June 30, 2012



On the Cover

Comparison of mega-cell and giga-cell reservoir models for the

predicted oil saturations. Mega-cell simulation of oil saturation

(upper image) and giga-cell simulation of oil saturation (lower

image), from Figs. 13a and 13b.

GigaPOWERS™ Team members (clockwise from top left):Abdulrahman Al-Mana, Abdulaziz Al-Baiz, Tareq Al-Shaalan,Ali Dogru, Nabil Al-Zamel, Annette Fugl, Ibrahim Khayyat,Usuf Middya, Larry Fung, Tom Dreiman and Jorge Pita. Notpictured: Werner Hahn, James Tan and Tom Byer.

The Saudi Aramco Journal of Technology ispublished quarterly by the Saudi Arabian OilCompany, Dhahran, Saudi Arabia, to providethe company’s scientific and engineeringcommunities a forum for the exchange ofideas through the presentation of technicalinformation aimed at advancing knowledgein the hydrocarbon industry.

Complete issues of the Journal in PDF formatare available on the Internet at:http://www.saudiaramco.com (click on “publications”).

SUBSCRIPTIONS

Send individual subscription orders, addresschanges (see page 70) and related questions to:

Saudi Aramco Public Relations DepartmentJOT DistributionBox 5000Dhahran 31311, Saudi ArabiaFax: +966/3-873-6478Website: www.saudiaramco.com

EDITORIAL ADVISORS

Mohammed S. Al-GusaierPresident, Vela International Marine Ltd.

Isam A. Al-BayatVice President, Power Systems

Abdulla A. Al NaimVice President, Exploration

Zuhair A. Al-HussainVice President, Drilling and Workover

Saad A. Al-TuraikiVice President, Southern Area Oil Operations

Abdullah M. Al-GhamdiGeneral Manager, Northern Area Gas Operations

EDITORIAL ADVISORS (CONTINUED)

Salahaddin H. DardeerManager, Yanbu’ Refinery

Mohammed A. AnsariProgram Director, Technology

Abdulmuhsen A. Al-SunaidSenior Engineering Consultant, EnvironmentalProtection

Omar S. AbdulhamidManager, Lab Research and Development Center

Samer S. AlAshgarManager, EXPEC ARC

CONTRIBUTIONS

Relevant articles are welcome. Submissionguidelines are printed on the last page.Please address all manuscript and editorial correspondence to:

EDITOR

William E. BradshawThe Saudi Aramco Journal of TechnologyRoom 2240 East Administration BuildingDhahran 31311, Saudi ArabiaTel: +966/3-873-5803E-mail: [email protected]

Unsolicited articles will be returned onlywhen accompanied by a self-addressedenvelope.

Khalid A. Al-FalihPresident & CEO, Saudi Aramco

Khalid I. AbubshaitExecutive Director, Saudi Aramco Affairs

Nasser A. NafiseeGeneral Manager, Public Relations

PRODUCTION COORDINATION

Robert M. Arndt, ASC

DESIGN

Pixel Creative Group, Houston, Texas, U.S.A.

ISSN 1319-2388.

© COPYRIGHT 2011 ARAMCO SERVICES COMPANYALL R IGHTS RESERVED

No articles, including art and illustrations, inthe Saudi Aramco Journal of Technology,except those from copyrighted sources, maybe reproduced or printed without thewritten permission of Saudi Aramco. Pleasesubmit requests for permission to reproduceitems to the editor.

The Saudi Aramco Journal of Technologygratefully acknowledges the assistance,contribution and cooperation of numerousoperating organizations throughout the company.

ATTENTION! MORE SAUDI ARAMCOJOURNAL OF TECHNOLOGY ARTICLESAVAILABLE ON THE INTERNET.

Additional articles that were submitted forpublication in the Saudi Aramco Journal ofTechnology are being made available online. Youcan read them at this link on the Saudi AramcoInternet Web site: www.saudiaramco.com/jot.

Documents

JOT Spring 2011 Aramco