o gj.co mhttp://www.ogj.com/articles/print/vo lume-111/issue-8/drilling-production/study-develops-decline-analysis-geo logic.html

BARNETT SHALE MODEL-1: Study develops decline analysis,geologic parameters for reserves, production forecast

John Browning, Scott W. Tinker, Svetlana Ikonnikova, Gürcan Gülen, Eric Potter, Qilong Fu, SusanHorvath, Tad Patzek, Frank Male, William Fisher, Forrest Roberts University of TexasAustin

Ken Medlock IIIRice UniversityHouston

A comprehensive study of the reserve and production potential of the Barnett shale integrates engineering,geology, and economics into a numerical model that allows f or scenario testing based on several technical andeconomic parameters. The study was conducted by the Bureau of Economic Geology (BEG) at the Universityof Texas at Austin and f unded by the Alf red P. Sloan Foundation.

In its base case, the study f orecasts a cumulative 45 tcf of economically recoverable reserves f rom theBarnett, with production declining predictably to about 900 bcf /year by 2030 f rom the current peak of about 2tcf /year.

The f orecast f alls in the mid to higher end of other known predictions f or the Barnett and suggests that it willcontinue to be a major contributor to US natural gas production through 2030.

This study by geologists, engineers, and economists resulted in a cohesive model of the Barnett, linkinggeologic mapping, production analysis, well economics, and development f orecasting.

The study will be presented in two parts: Part 1 summarizes the geologic characterization, per-well productiondecline analysis, and productivity t iering required to f eed into the detailed modeling of f uture reserve andproduction f orecasts. The conclusion (OGJ, Sept. 2, 2013) examines f ull f ield economics and production andreserve f orecasts.

Part 1 of f ers several unique contributions:

• Well-by-well analysis of production and calculation of estimated ult imate recovery (EUR) f or all wells.

• Development of a physics-based decline curve that closely describes Barnett well declines.

• Drainage explained by linear transient f low, result ing in decline rates inversely proportional to the square rootof t ime f or the f irst 5-8 years and later exponential decline as a result of interf racture interf erence within thewell drainage volume.

• Subdivision of the f ield into 10 productivity t iers, providing much-improved granularity f or reserve f orecastingand economics.

• Geologic mapping of porosity (PhiH) and net pay-zone thickness across the entire play. PhiH was determinedto be a key driver of well productivity.

• Calculation of volumetric original f ree gas in place (OGIPf ree) of 444 tcf f or the 8,000-sq-mile study area.

• Technically recoverable resources f or each square mile ranging f rom about 100 bcf /sq mile in the top tiers toless than 20 bcf /sq mile in the lower t iers, and f ield wide technically recoverable resources of about 86 tcf .

Study approach

The BEG study examines production data f rom all individual wells drilled 1995-2010 in the Barnett shale play inthe Fort Worth basin. The study compares its 2050 production f orecast to actual production through 2012.While other known assessments of the Barnett have relied on aggregated average production, of f ering a "top-down" approach to reserves and production f orecasting, the BEG study takes a "bottom-up" approach,starting with the production history of every well then determining what remains to be drilled under variouseconomic scenarios. The result is an unprecedented, comprehensive view of the f ield.

The study assesses natural gas and liquid production potential in 10 productivity t iers and uses them tof orecast f uture production. Development economics vary widely across the basin as a f unction of productionquality and liquids content.

The study's model accounts f or this granularity, allowing f or variations in natural gas price, drilling cost, gasplant processing economics, recovery f actors (RFs) f or each well, economic limit of each well, advances intechnology, and many other geologic, engineering, and economic parameters to determine how much naturalgas operators in the f ield will be able to extract economically f rom f uture wells.

Thickness and porosity af f ect reserves greatly, and natural gas and liquids prices are major f actors af f ectingproduction. The model suggests that sensit ivity to price is not overly dramatic, at least in the f irst severalyears of a f ormation's development, because many locations remain to be drilled in the higher-qualityproductivity t iers and higher- liquids regions.

A new method of estimating ult imate production f or each well, based on the physics of the system rather thanon mathematical decline-curve f itt ing, is integral to the project and should of f er a more accurate method off orecasting production declines in shale wells in other basins.1

Our Barnett shale study, which began in mid-2011, uses 15,144 wells whose production is through mid-2011 f orthe purpose of t iering and decline-curve analyses. In the production outlook model, we f orecast f ielddevelopment 2011-30 and run production f orecasts through 2050.

This approach allows us to benchmark our f orecast against actual 2011-12 drilling and production results—animportant vetting process. Although the model allows f or countless scenarios, we report on a base case of$4/Mcf natural gas and other relatively moderate technical and economic assumptions.

The base-case model f orecast 2,757 new wells would be drilled 2011-12 with a total of 3.90 tcf of natural gasproduced during the period by existing and newly drilled wells. Actual f ield results f or 2011 and 2012 were,respectively, 1,765 and 1,259 (3,024 total) new wells drilled and 2.09 and 2.07 tcf (4.16 tcf total) of natural gasproduced f rom the f ield.

The Barnett shale study workf low (Fig. 1) was developed to allow iterative input f rom all disciplines, ult imatelyleading to a rigorous production outlook model. This result provides some level of conf idence that the modeland its underlying assumptions are reasonable, at least in the early years. A model update that will include allwells drilled through 2012 and their production through mid-2013 will be run in 2014.

Geologic characterization

We chose a study area to encompass the extent of previous drilling within known geologic boundaries of thef ield. A total of 8,000 sq miles was included, although only 4,172 sq miles had been tested by drilling through2010. The Barnett thins to the west and south, and the boundaries of the study area were extended to includethe conceivable developable extent (Fig. 2).

We perf ormed a log-based assessment of the Barnett.2 Raster logs f rom 16,730 wells were available, 2,010 ofwhich were deep enough to view the f ull Barnett section, and only 420 had both density and neutron-porositylogs.

Of these, 146 wells with good log quality and caliper (measure of hole quality) and good spatial distributionwere selected. Major stratigraphic tops were picked in all wells, and the study f ocused on the lower Barnett, thetarget interval of the vast majority of production.

To the west the complete Barnett section is included in the pay zone, but to the east the Barnett is subdividedinto upper and lower members by the Forrestburg limestone f ormation. The lower Barnett thickens near itseast edge with the addition of lower-quality porosity zones and interspersed lime mudstones that could inhibitf racture growth during hydraulic- f racture completions.

Two cross sections of the Barnett shale show upper Barnett, Forrestburg limestone, upper part of lowerBarnett (nonpay zone), and lower part of lower Barnett (pay zone). Shaded areas of density- log porosity(DPHI) curve represent DPHI values <5%. Anomalously thick Barnett shale in a well in cross-section B-B' isinterpreted to have deposited in karst sinkhole or collapsed cave. Pay zone is highlighted in yellow. Locationsof cross sections are shown in Fig. 2. Source: Ref erence 2. (Fig. 3)

In this area, only the lower part of the lower Barnett is included in the pay zone (Fig. 3).

Density- log porosit ies across the pay zone were analyzed so that, when aggregated, they yielded adistribution average of 11.6% porosity (Fig. 4). This average was compared with crushed-core porosity f rom796 samples in f our wells,3 yielding an average porosity of 6.0%. The study used the ratio of log and coreporosit ies to create an adjustment f actor of 52% to adjust density- log porosity to core-equivalent porosity.

Maps were developed of adjusted porosity across the f ield, in addition to maps of net pay-zone thickness.The two maps were combined into a PhiH map indicating reservoir volume. The porosity- thickness map wasf ound to provide the best correlation to well productivity, a key element in predicting f uture f ield productionf rom undeveloped areas of the f ield. PhiH is clearly driven by the inf luence of pay-zone thickness, rather thanby more gradually varying porosity (Fig. 5).

Porosity contour map of Barnett Shale pay zone (lef t) uses DPHI values calibrated by multiplier of 0.52;contour interval = 0.01 (1%). Isopach map of Barnett Shale pay zone (center) is based on pay-zone intervalsidentif ied in GR and DPHI log curves of 146 wells. Thick trend exists f rom easternmost Parker County andeasternmost Wise and southern Denton counties through Tarrant County to northwestern Johnson County.Contour interval = 20 f t. DPHI*H contour map of Barnett Shale pay zone (right) shows that DPHI*H valuesvary f rom about 10 to 54. Contour interval = 5 porosity- f t. Source: Ref erence 2. (Fig. 5)

We recognize that the geologic description could have been enhanced greatly by analysis of seismic data to

We recognize that the geologic description could have been enhanced greatly by analysis of seismic data toidentif y f ault ing, karsting, and other anomalies across the f ield. Seismic analysis was beyond the scope of thestudy, however, and we think that PhiH mapping is adequate to explain the f irst- level geologic drivers ofproduction and to predict productivity across the f ield. The 3D seismic would be more crucial at an individual-prospect level.

Production decline; economic analysis

The study conducted individual decline analysis on all 15,144 wells drilled through 2010 to determine theirindividual EURs.1 Some of the key input variables in the study included:

• Base-well declines.

• Ef f ects of late- lif e deterioration in decline owing to interf racture interf erence within the well's drainage area.

• Ef f ects of attrit ion, recognizing that a percentage of wells have been lost each year as a result of aneconomic limit below which wells were shut in, a maximum 25-year lif e f or all wells and technology improvement.

The base case yields an expected lif etime EUR f rom 15,144 wells drilled through 2010 of 18.5 tcf . Forecastf ield cumulative production through 2012 is 12.1 tcf f rom a f orecast 17,901 wells. The f orecast is slightly lessthan actual total- f ield production through 2012 of 13 tcf and actual wells drilled of 18,821. These data providesome level of conf idence that the model and its underlying assumptions are reasonable.

The study developed a well-production-decline method on the basis of linear transient f low in the reservoir,1

which resulted in a per-well production decline inversely proportional to the square root of t ime f or the f irst 8-10 years of well lif e, f ollowed by exponential decline as interf racture interf erence began to af f ect production.

This approach was matched against all wells individually and against the f ull population of wells, demonstratinga root mean squared deviation f rom square-root of t ime f low of 8.584 (+/- 0.039) Mcf f or the mean horizontalwell with no apparent ref ractures, closely conf orming to the theoretical prediction (Fig. 6). We also examinedcumulative production f rom the well as a f unction of the log of t ime (Fig. 7).

A theoretical solution would yield a straight- line increase of cumulative production vs. log time untilinterf racture boundary conditions were reached within the well- f racture pattern, result ing in the predictedgentle downturn. The f ull derivation developed f or the study includes not only prediction of interf ractureef f ects, but also ef f ects of adsorbed-gas release in the f ormation, supplementing well production as reservoirpressures decline. Application of this method requires knowledge of Langmuir adsorption isotherms, which, f orthe Barnett, have relatively insignif icant ef f ects on production.

We normalized production f rom all 15,144 wells to the median EUR and then plotted cumulative production vs.log-time distribution, along with the study's theoretical solution (Fig. 8).

In addition, we conducted an independent econometric-data analysis of about 16,000 wells.4 Results of thispanel analysis conf irmed f ield-wide well decline rates consistent with those predicted by the study's declinemodel.

Across Barnett f ield, many wells have erratic production with unexplained production increases during theirlif etime. Some of these increases likely result f rom secondary ref racturing, commencement of artif icial lif t,compression additions, or other operational stimuli. Ref racturing (ref rac) occurs when an additional f racturetreatment is perf ormed on existing perf orations or additional perf oration intervals are added. Some analystsspeculate that ref racturing could be a signif icant additional source of reserves.

Our study mathematically identif ied and quantif ied stepwise changes in the month-to-month EUR calculationf or the well, using both Chow test and a statistical f orecast measurement error f rom the predicted trend wheresignif icant deviations were noted f or visual inspection. This step was perf ormed to distinguish between slopechanges, perhaps due to neighbor interf erence, and intercept shif ts, such as might result when a ref racoccurs. The process resulted in identif ication of wells f or visual inspection of the production data.

Because our study did not conf irm each potential case of ref racturing with a given operator, this result shouldbe considered preliminary. Given that caveat, the total impact f rom the identif ied potential ref ractures was lessthan 2% of f ield production, and in most cases per-well economics do not justif y the cost.

Reservoir quality tiering

Knowing the EUR and location f or every well in the f ield allowed us to determine productivity distributionacross the play.5 We chose to examine EUR instead of init ial production rate or early- lif e production rate andanalyzed well results using a 1-sq mile grid as the smallest geographic mapping element. The well populationcontained a variety of completion technologies that af f ect EUR. Theref ore, we f irst analyzed the relationshipbetween EUR and horizontal length and f ound that total EUR increases with horizontal well length.

Next we looked at the incremental EUR contributed by each additional 500 f t of horizontal well length. Ourapproach was to group the wells into bins of 500-f t increments (i.e., wells 1,000-1,500 f t length, then wells1,500-2,000 f t, so f orth). We f ound f or each bin the average EUR/f oot declined with horizontal length (Fig. 9).

Using this inf ormation we adjusted all horizontal wells to a normalized EUR as if they had been drilled aunif orm length (a 4,000-f t horizontal well length was chosen to normalize the data). The normalized EUR f or allwells was then subdivided into 300-f t long horizontal segments along the well path. Directional surveys f or allwells were plotted, and all length-normalized EUR/f oot values f or 300-f t long segments within each block wereaveraged.

The resulting average length-normalized EUR/f oot f or each block allowed f or a productivity ranking of allblocks having at least one productive well. These ranked blocks were then subdivided into 10 productivity t iers,with each tier containing the same number of blocks. These tiered blocks were then mapped, and the undrilledblocks were f illed on the basis of mathematical interpolation, biased by PhiH, to yield a f ull- f ield productivity-t ier map (Fig. 10).

Tiering indicates a core area of higher productivity in a pattern that closely corresponds to the PhiH map (Fig.5), indicating that reservoir pore volume is a key driver of productivity and that, within PhiH, thickness has thegreatest inf luence. We ref er to this map as a rock-quality map because of this close correspondence.

That said, the reservoir has considerable heterogeneity, with better perf ormance blocks interspersed withpoorer perf ormance blocks and sometimes even better perf orming wells next to poorer perf orming wells withinthe same block. The f ield f lanks have relatively low productivity where the reservoir thins to the west andsouth. The maps indicate high reservoir quality underlying the city of Fort Worth, highlighting resources thatwould be challenging to develop.

Acknowledgements

This research was conducted within the Bureau of Economic Geology in the Jackson School of Geosciencesat the University of Texas at Austin. The research team consisted of the authors as well as Likeleli Seit lhekoand Katie Smye. We thank IHS and DrillingInf o f or access to their databases. We also thank members of the USEnergy Inf ormation Administration's Of f ice of Petroleum, Natural Gas & Biof uels Analysis f or thoughtf ulreviews of the manuscript.

References

1. Patzek, T.W., Male, F. and Marder, M., "Simple models f or gas production f rom hydrof ractured shales," AAPGBulletin, publication date to be announced.

2. Fu, Q., Horvath, S., Potter, E., Roberts, F., Tinker, S. W., Ikonnikova, S., Fisher, W., and Yan, J., "Log-basedthickness and porosity mapping of the Barnett shale Play, Fort Worth Basin, Texas: A proxy f or reservoir qualityassessment," AAPG Bulletin, publication date to be announced.

3. Kale, S.V., Rai, C.S., and Sondergeld, C.H., "Petrophysical Characterization of Barnett shale," SPEUnconventional Gas Conf erence, Pittsburgh, Pa., Feb. 23-25, 2010.

4. Medlock III, K.B., and Seitlheko, L., "Panel data analysis of Barnett shale production," under review.

5. Ikonnikova, S., Browning, J., Horvath, S., and Tinker, S. W., "Well Recovery, Drainage Area, and Future Drill-wellInventory: Empirical Study of the Barnett shale Gas Play," SPE Reservoir Evaluation & Engineering, underreview.

The authors

Scott W. Tinker (scott.t inker@beg.utexas.edu) is the director of the Bureau of Economic Geology at theUniversity of Texas at Austin, the State Geologist of Texas, and holds the Allday Endowed Chair and is actingAssociate Dean of Research in the Jackson School of Geosciences at the university. He spent 17 years in theoil and gas industry bef ore joining UT in 2000. He is past president of American Association of PetroleumGeologists, Association of American State Geologists, and Gulf Coast Association of Geological Societies. Heis a Geological Society of America Fellow and holds appointments on the National Petroleum Council andInterstate Oil and Gas Compact Commission among other boards. Tinker holds a PhD in geological sciencesf rom the University of Colorado, an MS in geological sciences f rom the University of Michigan, and a BS ingeology and business administration f rom Trinity University.

John Browning (txbrowning@gmail.com) has been a senior research f ellow since 2010 at the BEG, havingretired f rom ExxonMobil af ter 33 years. He holds a BS (1977) in mechanical engineering f rom the University ofTennessee.

Svetlana Ikonnikova (svetlana.ikonnikova@beg.utexas.edu) is a research associate and energy economist inthe BEG. She holds an MS (2003) in applied mathematics and physics f rom the Moscow Institute of Physicsand Technology, Moscow, and a PhD (2007) in economics f rom Humboldt University of Berlin.

Gürcan Gülen (gurcan.gulen@beg.utexas.edu) is a research associate and energy economist at the BEG inHouston. He is a member of the United States Association of Energy Economics (USAEE), the InternationalAssociation of Energy Economics, and the Society of Petroleum Engineers. He holds a PhD (1996) ineconomics f rom Boston College and a BA (1990) in economics f rom Bosphorus University in Istanbul.

Tadeusz Patzek (patzek@mail.utexas.edu) is the Lois K. and Richard D. Folger Leadership Prof essor and chairof the Petroleum and Geosystems Engineering Department at the University of Texas at Austin. He also holdsthe Cockrell Family Regents Chair No. 11. He was prof essor of geoengineering at the University of Calif ornia,Berkeley, 1990-2008, and bef ore that was a researcher at Shell Development f or 7 years. Patzek is a memberof the Ocean Energy Saf ety Advisory Committee f or the US Department of Interior's Bureau of Saf ety andEnvironmental Enf orcement and of the SPE. Patzek holds an MS (1974) and PhD (1980) in chemical engineeringf rom the Silesian Technical University in Poland.

Eric Potter (eric.potter@beg.utexas.edu) is program director f or energy research at the BEG, joining in 2001af ter 25 years with Marathon Oil Co. as an exploration geologist and as geoscience technology manager atMarathon's technology center in Denver. He holds a BA (1972) in earth science f rom Dartmouth College and anMS (1975) in geology f rom Oregon State University.

Qilong Fu (qilong.f u@beg.utexas.edu) is a geologist with the BEG, joining in 2008. He holds a BS in geologyf rom the Changchun College of Geology (Jilin University) in China, an MS in geology f rom the Institute of

Geology, Chinese Academy of Sciences, and a PhD in geology f rom the University of Regina, Sask.

Susan Horvath (susan.horvath@beg.utexas.edu) is a research scientist associate at the BEG. Previously, sheworked as a GIS analyst f or the Arkansas Geological Survey. She holds a BS (2002) and an MSc (2007) ingeographic inf ormation systems f rom Eastern Michigan University. She is a member of AAPG, the Urban andRegional Inf ormation Systems Association, and Supporting Women in Geography and GIS.

Kenneth B. Medlock III (medlock@rice.edu) is James A. Baker III and Susan G. Baker Fellow in Energy andResource Economics at the James A. Baker III Institute f or Public Policy and senior director of the BakerInstitute's Center f or Energy Studies at Rice University, Houston. He is also vice-president of academic af f airsf or the USAEE. Medlock holds a PhD (2000) in economics f rom Rice University.

Frank Male (f rank.r.male+ut@gmail.com) is a graduate student in the Center f or Nonlinear Dynamics and theDepartment of Physics at the University of Texas at Austin. Bef ore joining the university in 2010, he workedwith the Max Planck Institute f or Dynamics and Self -Organization in Göttingen, Germany, as a computationalmicrof luidics researcher. Male holds a BS in physics and BA (2009) in polit ical science f rom Kansas StateUniversity.

Forrest Roberts (roberts.f orrest@gmail.com) is a graduate research assistant at the BEG, working on an MS ingeology. He worked as a geology intern with Apache Corp. in Houston during summer 2012. He holds a BS(2011) in geological sciences f rom Brigham Young University and is a member of the AAPG and the Society ofExploration Geophysicists.

William L. Fisher (wf isher@mail.utexas.edu) holds the Barrow Chair of Mineral Resources and is a prof essor inthe department of geological sciences, University of Texas at Austin. He f ormerly served as director of theBEG, director of the Geology Foundation, and dean of the Jackson School of Geosciences, all of theUniversity of Texas at Austin. Fisher holds a BS and DSc (Hon.) f rom Southern Illinois University, an MS andPhD in geology f rom the University of Kansas, a Deng (Hon.) f rom the Colorado School of Mines, and receivedthe Presidential Citation (equivalent to an honorary doctorate) f rom the University of Texas at Austin.