BoreholeImage log

Borehole Image Log Technology:Application Across the Exploration

and Production Life Cycle

Michael PoppelreiterQatar Shell Research and Technology Centre,

Doha, Qatar

Carmen Garca-CarballidoMaersk Oil North Sea, UK Ltd., Altens,

Aberdeen, United Kingdom

Martin KraaijveldPetroleum Development Oman,Muscat, Sultanate of Oman

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INTRODUCTION

The ever-increasing energy demand requires re-

covery from existing fields to be maximized, a pro-

cess mostly dependent on accurate reservoir models.

Borehole imaging is one of the fastest and most pre-

cise methods for collecting subsurface data (Figure 1).

Borehole imaging can feed even millimeter-scale in-

formation into these models (Paillet et al., 1990). In-

creasingly, borehole images can be transmitted and

integrated into reservoir models while drilling, which

is an important step toward real-time field optimiza-

tion (Sparkman, 2003). Recent advances in modeling

software allow the fully integrated use of image data

in reservoir models for the first time. As a result, the

status of borehole image technology is shifting from

being a niche application to being a key component of

both static and dynamic reservoir models. Techno-

logical advances in borehole imaging during the

past 5 yr have facilitated these developments. One

important area of progress is the introduction of high-

resolution oil-base imagers (Pavlovic, 2002). The qual-

ity of recent images in oil-base mud systems comes

close to wireline image resolution. Even more signif-

icant is the development of high-resolution logging-

while-drilling (LWD) tools (Ritter et al., 2004) that now

rival traditional wireline images. The latest generation

of LWD imaging tools can detect high-permeability

streaks only a few decimeters thick and can track them

for long distances using advanced, image-based geo-

steering technology (Prosser et al., 2006).

Borehole imaging is now on its way to becoming

a mature technology, and it is used in increasingly

advanced applications (Prensky, 1999; Inaba et al.,

2003). Good examples of this include the integration

of dipmeter and borehole image data with nuclear

magnetic resonance logging (Mathis et al., 2004; Daoud

et al., 2006), and three-dimensional (3-D) resistivity

profiles derived from image data to improve the iden-

tification of high-permeability intervals that standard

wireline tools do not recognize (Aplin et al., 2003).

When image log data are coupled with sophisticated

interpretation techniques, complex reservoir con-

nectivity problems can be solved (Bal et al., 2002).

Chapter 1

Poppelreiter, M., C. Garca-Carballido, andM. Kraaijveld, 2010, Borehole image logtechnology: Application across the explo-ration and production life cycle, in M.Poppelreiter, C. Garca-Carballido, and M.Kraaijveld, eds., Dipmeter and boreholeimage log technology: AAPG Memoir 92,p. 113.

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Copyright n2010 by The American Association of Petroleum Geologists.

DOI:10.1306/13181274M923406

Nowadays, crisp images are increasingly being gen-

erated in all borehole environments (Figure 2). This

is fostering rapid advances in geomechanical imaging

applications (Zoback, 2007). Geomechanical analyses

of image log data can greatly improve fluid-flow pre-

dictions in fractured reservoirs (Barton et al., 1997) and

can also help optimize wellbore trajectories (Finkbeiner

et al., 2000). Because they can delineate geobody ori-

entation, image logs can also provide a critical well-

bore calibration of 3-D seismic features (Hesthammer

and Fossen, 2003).

Although the value of widespread application of

imaging technology is widely acknowledged, still,

too few geoscientists are sufficiently familiar with

this type of data. This volume aims to provide some

guidance in borehole image principles and workflows

and to illustrate the value of image data for managing

and modeling the subsurface.

DIPMETER AND BOREHOLE IMAGELOGS IN THE EXPLORATION AND

PRODUCTION LIFE CYCLE

The organization of this volume reflects the stages

of field development in the exploration and produc-

tion (EP) life cycle (Figure 3). Thus, dipmeter and

image log applications are discussed from job plan-

ning to complex field development projects.

Planning, Acquisition, Quality Control,and Management of Dipmeter and BoreholeImage Data

Borehole image acquisition includes the planning

of logging jobs and the deployment of appropriate

data management systems and quality control guide-

lines (Serra, 1989). Such procedures help improve the

quality of both image acquisition and image interpre-

tation throughout the exploration, appraisal, develop-

ment, and production stages; this is important because

an interpretation can only be as good as image quality

and resolution permit. To acquire dipmeter and bore-

hole image logs of optimum quality, it is essential to

have the operator work closely with the service com-

pany to select the appropriate imaging tool, consid-

ering the required geological resolution, mud type

and weight, and expected formation characteristics.

Lagraba, Hansen, Spalburg, and Helmy (Lagraba et al.,

2010) discuss these preparatory considerations in

Chapter 2, including the value of information analysis.

Management of dipmeter and borehole image data

is another significant challenge faced by operating and

service companies. All the major oil companies have

corporate databases that, together, contain thousands

of dipmeter and borehole image logs, with legacy

data commonly of unknown quality. The dipmeter

and image data sets are constantly being upgraded

as new tool types are introduced or existing data sets

are (re)processed and (re)interpreted (Aplin et al.,

2002). This places a permanent pressure on data man-

agement requirements. However, having a fit-for-

purpose system to manage image data can add tre-

mendous value to the logs that are acquired. The

accessibility of quality-controlled logs is fundamen-

tal for real-time operations as well as reservoir man-

agement. Optimized data flow and storage systems

are outlined by Garca-Carballido, Boon, and Tso, who

present a structured approach to the quality control of

image data in Chapter 3. Consistent quality control

checkpoints and templates as discussed in this chapter

is recommended for general use. They are prerequisites

Figure 1. Evolution of dip-meter technology from a sim-ple three-arm dipmeter to ahigh-resolution Fullbore For-mation MicroImager (FMITM,Schlumberger) over the last40 yr (courtesy of Schlumber-ger). HDT = high resolutiondipmeter tool; SHDT = strati-graphic high resolution dip-meter tool; FMS = formationmicroscanner. (FMS). Schlum-berger manufactures thesetools.

2 Poppelreiter et al.

for assuring data integrity during subsequent reser-

voir modeling stages. A practical addition to this pro-

cess is the use of a consistent but flexible corporate

naming convention. This convention supports data

transparency and assures that interpretations are easy

to share among the geoscience community.

Chapter 4 outlines data processing and interpreta-

tion principles for dipmeter and borehole image logs.

Hansen and Buczak provide a step-by-step guide

through this process, with numerous illustrations

(Figure 4). The expression one persons information is

the others noise has been coined to highlight the im-

portance of having a genetic, rock-calibrated process-

ing and interpretation workflow (Adams et al., 1990).

The oil industry can make use of neatly quality-

controlled, processed, and interpreted dipmeter and

Figure 3. Examplesof geomechanical ap-plications of dipmeterand borehole imagetechnology in the fieldcycle (courtesy of Bartonand Moos, 2010).

Figure 2. Although oil-base mud revolutionized drilling and borehole properties, it had detrimental effects ondipmeter and image log quality for many years. Nowadays, tools such as the EARTH ImagerSM (Baker Hughes/BakerAtlas) let geoscientists see through oil-base mud (courtesy of Maddock and Ravnas, 2010).

Borehole Image Log Technology: Application Across the Exploration and Production Life Cycle 3

borehole image data in new ways for static reservoir

modeling applications. The ease of the seamless

integration of image data with any other data set, as

well as of data management, is shown in Chapter 5,

and it is a key driver for the current upsurge in image

log utilization. Poppelreiter, Crookbain, Sapru, and

Lawrence present an overview of current applica-

tions of dipmeter and borehole image interpreta-

tions in reservoir models (Figure 5).

Exploration

Borehole image logs now have an established place

in most exploration wells. For hazardous boreholes

that cannot be cored, they are considered a must-

have. Arguably, one of the largest advances that

dipmeter and borehole image technology has ex-

perienced in the last 15 yr is its application to geo-

mechanics (Figure 6). Barton and Moos provide a com-

prehensive summary of geomechanical applications

in Chapter 6. As shown in this chapter, the predic-

tion of maximum hydrocarbon column height in fault

block structures is an important application of geo-

mechanics during the exploration stage. Such analy-

ses can potentially reduce the risk of encountering

breached fault seals with uneconomic hydrocarbon

volumes (Wiprut and Zoback, 2002). Additional con-

fidence in such analysis is provided when imaging is

combined with hydrocarbon indicators from seismic

data. However, the full use of geomechanical princi-

ples in reservoir modeling is not yet a reality, partly

because a software package suitable for widespread

use is not available. Nevertheless, some promising

studies have looked at developing geomechanical al-

gorithms within standard modeling packages (Wynn

et al., 2005; Poppelreiter et al., 2009).

The exploration portfolio of many oil companies

increasingly contains structures that are poorly im-

aged on seismic data. A significant contribution of dip-

meter and borehole image logs when dealing with such

structurally risky trap configurations is in the delinea-

tion of the dip and azimuth of the top of the reservoir.

Chapter 7 presents a case study of a salt flank structure

by Garca-Carballido, Styles, and Poppelreiter, which is

a good example of this. The workflow proposed can

generally be applied to areas with poor seismic quality

to reduce top structure uncertainty. This workflow

also emphasizes the value of using dipmeter data that

were acquired during the exploration stage to help

well planning during the appraisal or development

stage. Moreover, dipmeter and borehole image data

are also used to determine paleocurrent direction and

fairway reservoir trends to reduce reservoir risk. This

technique of sand fairway mapping tends to increase

exploration success, particularly within mature hydro-

carbon provinces where calibration points are at hand

(Heine, 1993).

Chapter 8 outlines reservoir characterization by

EARTH Imager (Baker Hughes/Baker Atlas) data from

a wildcat well. Maddock and Ravnas describe image

acquisition under high-temperature, high-pressure

conditions in a well that was drilled with oil-base mud.

Figure 4. Data proces-sing converts raw dip-meter and boreholeimage data into imagesthat show the boreholewall similar to a photo-graph. The figure showsraw microresistivitydata in wiggle form andconverted data to imageform using simple (mid-dle two tracks) andhistogram-based colormapping (right two tracks)(courtesy of Hansenand Buczak, 2010).


The images acquired are superb examples not only of

the value of dipmeter and borehole images acquired

in exploration wells, but also of the limitations.

For such interpretations to work, however, rock

calibration is essential (Figure 7). The nonuniqueness

of dipmeter and borehole image data prevents a di-

rect conversion of image patterns into reservoir mod-

els. Rock calibration based on cuttings, core, or out-

crops is indispensable. Conceptual models, like the

ones by Donselaar and Schmidt that are shown in

Chapter 9, are a prerequisite for making meaning-

ful use of image data for this kind of application.

Chapter 7 and Chapter 19 both show examples of the

limitations of using uncalibrated dipmeter and bore-

hole image interpretations with fluvial deposits.

APPRAISAL

One of the prime objectives of acquiring dipmeter

and borehole image logs during the appraisal stage

is to estimate the structural and stratigraphic com-

plexity of a field (Adams et al., 1990; Hurley, 1996; Bal

et al., 2002). An example of how outcrop-calibrated

borehole image data can be turned into a geological

model is shown by Slatt and Davis in Chapter 10. The

examination of calibrated images from behind

outcrop logging revealed groups of diagnostic fea-

tures in deep-water depositional systems identifi-

able on image logs. Genetic elements interpreted from

borehole images have specific geometries, internal

architecture, and properties that can be used to con-

strain conceptual reservoir models. However, limita-

tions in resolution are also highlighted by the authors.

This methodology can be applied to the substantial

core sections that are commonly acquired during the

appraisal stage. They provide critical calibration re-

garding depositional environment, diagenesis, and

reservoir architecture (Prosser et al., 1999).

However, structural features, like fractures, are

commonly less well calibrated at the appraisal stage.

Fractures tend to be undersampled in vertical wells.

Optimizing well design and placement, selection

of appropriate mud systems that reduce rig down

time, and improved well completion efforts depend

on image-based geomechanical models (Barton and

Zoback, 1998; Rahim et al., 2005). Geomechanical

models are particularly useful during the appraisal

stage when the drilling experience is still limited, and

these models can achieve substantial cost savings.

Figure 5. Integra-tion of imaging in-terpretations withopen-hole logs, seis-mic data, and othermethods to establishthe critical orientationand flow behaviorof fracture corridorsof a producer-injectorpair (courtesy ofPoppelreiter et al.,2010). FMITM = Full-bore FormationMicroImager (Schlum-berger); Prod. = pro-ducer well; Inj. =injector well; So = oilsaturation; Phi =porosity.


DEVELOPMENT

At the development stage, image log interpreta-

tions are applied to determine the net-to-gross ratio

in sand-shale intercalations (Hackbarth and Tepper,

1988), analyze reservoir connectivity, and character-

ize faults, and for a large variety of other applications

besides those mentioned here. Reservoir architecture

and connectivity are of particular interest because

they can impact the number and type of wells in a

development (see Figure 8). Barton, OByrne, Pirmez,

Prather, van der Vlugt, Alpak and Sylvester (Barton

et al., 2010) present an integrated workflow for het-

erogeneous deep-water depositional systems in Chap-

ter 11. This workflow covers all aspects from outcrop

analysis of key flow barriers, i.e., channel-base drapes

in a deep-water depositional system, to flow barrier

reservoir modeling and simulation. Identification of

these baffles, which are just a few centimeters thick, is

only possible with a combination of core, logs, and

dipmeter or borehole image logs. The chapter demon-

strates the major impact of these volumetrically in-

significant shales on fluid flow and on the develop-

ment of a reservoir.

Vertical wells, which are commonly preferred

during appraisal, tend to undersample structural

features. Carbonate reservoirs are particularly sen-

sitive to this, because it is commonly only after the

interpretation of image logs and dynamic data that

these reservoirs are identified as fracture plays (Bell

et al., 1992). Mattioni, Chauveau, Fonta, Ryabchenko,

Sokolov, Mukhametzyanov, Shlionkin, Zereninov, and

Bobb demonstrate the value of image logs for fracture

characterization in Chapter 12. The authors apply the

concept of mechanical stratigraphy to a single, slightly

deviated well in a dolomite reservoir. They translate

a careful investigation of lithology, bed architecture,

and stratigraphy into a conceptual fracture model and

quantitative fracture relationships. Their systematic,

iterative workflow is complemented by the construc-

tion of a discrete fracture network model (Trice, 1999).

PRODUCTION

Field and well optimization, and reservoir man-

agement, are key processes for maximizing produc-

tion while limiting expenditure. A robust surveillance

plan for the production stage commonly includes dip-

meter and borehole image logs. The number of these

data sets depends on the actual reservoir complexity

(Figure 9). In matrix-dominated reservoirs, it may

only be necessary to acquire image logs in key wells.

Also, conversely, fractured reservoirs can only be

described adequately after horizontal development

wells have been drilled and a representative number

of borehole image logs have been acquired (Adams

et al., 1999).

An intelligent well design that connects additional

permeable reservoir facies can enhance production

in some reservoirs. In Chapter 13, Lofts and Morris

describe a sophisticated geosteering method to maxi-

mize the penetration length of a reservoir using the

latest LWD imaging tools. Modern LWD tools pro-

vide an operating company with real-time informa-

tion of a quality that rivals wireline tools. Lofts and

Morris discuss how to plan and implement advanced

geosteering, combined with real-time reservoir model

updates, in detail in this chapter. The image quality they

Figure 6. Drilling-induced tension fractures (blue bar)showing the orientation of the maximum horizontalstress direction (courtesy of Barton and Moos, 2010).


Figure 7. Calibration of im-age logs with rock (e.g., cut-tings, core, and outcrop) isessential to convert imagesinto meaningful geologicalinterpretations (courtesy ofXu et al., 2009).

Figure 8. Applica-tion of dipmeterand image log datainterpretation resultsfor facies identifica-tion and well corre-lation (courtesy ofBarton et al., 2005,permission to re-print from Shell EP).


present suggests that imaging in LWD mode is set to

change the way complex reservoirs are developed.

At the production stage, powerful visualization

technology can be used to delineate structural trends

in fractured reservoirs. In Chapter 14, Richard and

de Pieri demonstrate the power of visualization and

data integration as a platform for discrete fracture

models.

Davatzes and Hickman outline fracture distribu-

tion in a geothermal field in fractured basement,

using detailed observations and data integration, in

Chapter 15. Such models are used as inputs to simu-

lations that allow a better understanding of fluid flow;

i.e., they clarify the extent of high-permeability layers,

thief zones, baffles, and barriers (Moeck et al., 2007).

Borehole image data sets can be combined with data

from a variety of other sources to model and manage

the reservoir and, possibly, influence the field devel-

opment strategy.

Image logs are also indispensable in reservoirs

with complex pore structure, such as carbonate res-

ervoirs. The characteristic (very) high resistivity that

these reservoirs commonly display has a detrimental

effect on image quality (see Figure 10). In Chapter 16,

Chitale, Johnson, Entzminger, and Canter describe a

recently introduced tool that can image the diagenet-

ically altered zones in carbonates that can act as flow

barriers or high-permeability zones, where subtle re-

sistivity contrasts have commonly made imaging diffi-

cult in the past. Moreover, the improved interpretation

Figure 9. Fracture characterization using Schlumbergers Fullbore Formation MicroImager (FMI) data in conjunctionwith open-hole logs to establish an e-facies scheme tuned to detect at least some structural features. APLC = logmeasure porosity; SGR = standard gamma ray; THOR = thorium; MDT_F = modular formation dynamics tester.


software that is also described in this chapter shows

how to convert images into Lucia-type permeability

classes.

Another aspect of image-based carbonate reser-

voir characterization is pore-type partitioning (Akbar

et al., 1995). The separation of touching and non-

touching vug systems with their very different per-

meabilities (Newberry et al., 1996), which is particu-

larly important in this regard, is described by Xu in

Chapter 17 (Xu, 2010). The methodology can lead to a

much improved permeability prediction with very

high resolution, so it can enhance the predictability

of reservoir models (Lovell et al., 1997).

The complexity of fractured reservoirs and the im-

portance of dynamic calibration of features interpreted

from image logs are presented in Chapter 18 by Fol-

lows, Beck, Farmer, Al Salmi, de Bruijn, De Pieri, and

Welling. The authors highlight the importance and

power of having an integrated reservoir surveillance

strategy. This chapter also emphasizes the complex

flow behavior of fracture systems and demonstrates

that the dynamic properties of fractures cannot al-

ways be derived from image logs alone (Nicholls et al.,

1999). This chapter also shows that the overall dy-

namic behavior of a fracture class can be estimated by

its static properties. The flow in individual fractures,

Figure 10. Integration of image log data with petrophysical data to characterize permeability streaks (courtesy ofChitale et al., 2010). GR = gamma ray; CAL1 = caliper1; PE = photoelectric factor; SPI = percent fraction of thetotal (crossplot) porosity, which is secondary vugular; SPHI = secondary porosity; XPHI = crossplot porosity;AMPEQ = static enhanced vector of the XRMI electrodes total signal (amplitude of the conductivity); VSND_AMP =sand fraction computed from the XRMI AMPEQ; FRAC_IMG = fractures from image log; PAY_IMAG = net reservoirflag from image log; PAY_LOG = net reservoir flag from open-hole log; RES = resistivity; XRMI = X-tended RangeMicro Imager (XRMITM, manufactured by Halliburton).


however, can vary widely. Integration of image logs

(see Figure 11) and production logs leads to an ap-

propriate dynamic characterization of the reservoir.

Samantray, Kraaijveld, Bulushi, and Spring pre-

sent the fully integrated use of dipmeter or borehole

image data for field development planning in a clus-

ter of fluvial reservoirs in Chapter 19. They describe a

methodology that can be applied generally to brown

field developments and mature assets. Their study

is also an important example of how the value of

legacy data sets, accumulated in their thousands by

national and international oil companies, can be re-

alized using them as inputs for reservoir models.

PERSPECTIVES

This volume combines a presentation of the fun-

damentals of dipmeter and borehole image technol-

ogy with descriptions of state-of-the-art applications.

The need to (iteratively) integrate image data with

other data sources is a recurring theme. Integration

requires better software, and several tools are nearly

ready to be put on the market. The next generation of

modeling software is expected to bridge the gap be-

tween stand-alone specialist borehole image process-

ing and interpretation tools and plug-ins for main-

stream subsurface interpretation software. This may

put borehole imaging on the desktop of large numbers

of geoscience and petroleum engineering profession-

als, including seismologists, geologists, petrophysi-

cists, reservoir engineers, production technologists,

and well engineers.

The next generation of image interpretation soft-

ware is also expected to provide standard geome-

chanical modules that again will be a significant fac-

tor in future generations of reservoir models. Tool

development is likely to move toward improved ver-

tical resolution, larger borehole coverage, and imaging

Figure 11. Visualization of borehole images with seismic and other data as a powerful way to establish structuraltrends, geometries, and flow properties. Such relationships can be used to build fracture realizations as part of static ordynamic reservoir models (courtesy of Richard and de Pieri, 2010).


in all possible mud systems. Soon multisensor and

multiarray imaging tools will facilitate data integra-

tion during data acquisition in the subsurface, using

different data acquisition methods (Badir, 2007). This

will break the present dominance of resistivity and

acoustic devices. New technology, such as the re-

cently introduced intelligent drill pipe (Reeves et al.,

2005), is likely to further enhance data transmission

to the surface.

All of these developments will pave the way for

imaging to shift from qualitative to quantitative petro-

physical interpretations (Kraaijveld and Epping, 2001),

opening up a completely new range of petrophysical

interpretation methodologies.

We hope that this volume sparks enthusiasm for

dipmeter and image log technology and provides a

useful reference both for the novice and for the ex-

perienced subsurface professional.

ACKNOWLEDGMENTS

The compilation of this volume was not possible with-out the contributions from many borehole image special-ists across the industry and academia. The editors are mostgrateful for the time and effort of the numerous reviewers:Graham Aplin, Mike Ashton, Goos Bakker, HomeroCastillio, Andrew Cook, Freddy Curby, Quintin Davies,Andy Duncan, Paul Elliot, Willem Epping, Roddy McGarva,Caroline Clark, Kelly Hazlip, Vincent Hilton, Jean-BernardJoubert, Christophe Mercadier, Alison Mabillard, JohnMarshall, Ron Nelson, Gerard Niks, Christian Ottesen,Jeremy Prosser, Fritz Rambow, Malcolm Rider, SteveRogers, Shaun Sadler, Lena Thrane, Gerhard Templeton,Robert Trice, Manoj Vallikkat, and Deborah Walker. Thisvolume was compiled thanks to these specialists and ShellsBorehole Imaging Team. The authors are also grateful foreditorial comments by Peter Scott-Wilson and SuzannaAtkinson on the introduction.

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