40 Oilfield Review

Resolving Carbonate Complexity

Assessing basic rock properties using traditional logging suites—usually a straight-

forward process in sandstone reservoirs—may be difficult or impossible in carbonate

reservoirs. Also, when dealing with carbonates, determining optimal locations for

new wells from petrophysical analysis often becomes little more than a statistical

exercise. However, new tools, techniques and interpretation methodologies are

helping petrophysicists unravel the complexities posed by carbonate reservoirs.

Equipped with this information, operators are able to drill and produce these reser-

voirs while better managing uncertainty.

Mariam Ibrahim Al-MarzouqiSultan BudebesEmad SultanAbu Dhabi Marine Operating CompanyAbu Dhabi, UAE

Iain BushGatwick, England

Roger Griffiths Kais B.M. GzaraRaghu RamamoorthyAbu Dhabi, UAE

Alexis HusserSugar Land, Texas, USA

Ziad JehaJuergen RothAhmadi, Kuwait

Bernard MontaronBeijing, China

Srinivasa Rao Narhari Sunil Kumar SinghKuwait Oil CompanyAhmadi, Kuwait

Xavier Poirier-CoutansaisMabruk Oil CompanyTripoli, Libya

Oilfield Review Summer 2010: 22, no. 2. Copyright © 2010 Schlumberger.For help in preparation of this article, thanks to Lisa Stewart, Cambridge, Massachusetts, USA; and Joelle Fay, Gatwick, England.AIT, Carbonate Advisor, DeepLook-CS, EcoScope, ECS, FCM, FMI, HRLA, Litho-Density, MD Sweep, Petrel, Q-Land, Sonic Scanner and SpectroLith are marks of Schlumberger.

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Summer 2010 41

Characterizing and evaluating carbonate reser-voirs from conventional logging data can be daunting. Traditional approaches that work per-fectly well for determining basic petrophysical properties in siliciclastics—such as porosity, saturation, permeability and rock mechanical properties—may yield inaccurate results in car-bonates. In addition to the difficulties in evaluat-ing rock properties, many carbonates have lateral structural heterogeneities; rock properties vary greatly across the field. Drilling to maximize pro-duction can thus become a statistical exercise: Drill enough wells and some will be successful.

Experts estimate that 60% of the world’s oil reserves, as well as vast quantities of natural gas, lie in carbonate reservoirs. The rewards for deci-phering these enigmatic formations are very attractive. But to do so, petrophysicists and engi-neers who evaluate and produce hydrocarbons from carbonates have learned that they must use methods that differ substantially from those used for sandstones. Fortunately, new tools are avail-able that increase analysts’ reservoir understand-ing and decrease risks associated with field development and reservoir management.

This article describes several recently intro-duced techniques, beginning at the drill bit and extending to fieldwide seismic studies that strive to clarify carbonate complexity. Included are advances in logging-while-drilling (LWD) technol-ogy that help geologists overcome difficulties they encounter evaluating carbonates when using con-ventional logging suites. We also review an inte-grated software workflow that addresses characteristics unique to carbonates. In addition, a seismic workflow method is presented that, com-bined with other data sources, identifies high-quality reservoir sections by detecting fracture corridors. Case studies from the Middle East dem-onstrate applications of these techniques.

The Problem with CarbonatesCarbonate sediments differ from siliciclastics in nearly every aspect: origin, deposition, diagene-sis, oil filling and evolution.1 Because abundant examples exist in the literature describing these differences, it might seem that carbonates are so well understood that new techniques would pro-vide only incremental assistance in their evalua-tion. However, the problems experienced by log analysts evaluating carbonates still provide sig-nificant opportunities for the development of new technologies and interpretation methods.

The problem is not that carbonates are poorly understood; geologists and petrophysicists have been studying and describing them since the dawn of the oil industry. They have developed

numerous classification systems that focus on particular carbonate peculiarities, such as tex-ture, pore size and internal rock structure (above).2 These efforts, however, do not equate to understanding specific reservoir rock properties in a given well or field.

Difficulties begin with quantifying basic in situ mineral, fluid and textural properties using conventional logging tools. Petrophysicists use these log data to characterize and identify quality reservoir rocks and guide drillers to the best pro-ducing zones. Because of the complexities of car-bonate reservoirs, evaluation programs often rely on conventional coring to decipher heterogene-ities in rock properties. Coring provides lithology, qualitative and quantitative estimation of porosity

1. For more on carbonates and carbonate evaluation: Akbar M, Petricola M, Watfa M, Badri M, Charara M, Boyd A, Cassell B, Nurmi R, Delhomme J-P, Grace M, Kenyon B and Roestenburg J: “Classic Interpretation Problems: Evaluating Carbonates,” Oilfield Review 7, no. 1 (January 1995): 38–57.

Akbar M, Vissapragada B, Alghamdi AH, Allen D, Herron M, Carnegie A, Dutta D, Olesen J-R, Chourasiya RD, Logan D, Stief D, Netherwood R, Russell SD and Saxena K: “A Snapshot of Carbonate Reservoir Evaluation,” Oilfield Review 12, no. 4 (Winter 2000/2001): 42–60.

Ahr WM, Allen D, Boyd A, Bachman HN, Smithson T, Clerke EA, Gzara KBM, Hassall JK, Murty CRK, Zubari H and Ramamoorthy R: “Confronting the Carbonate Conundrum,” Oilfield Review 17, no. 1 (Spring 2005): 18–29.

2. For more on carbonate classification systems: Scholle PA and Ulmer-Scholle DS: “Carbonate Classification: Rocks and Sediments,” in Scholle PA and Ulmer-Scholle DS (eds): A Color Guide to the Petrography of Carbonate Rocks: Grains, Textures, Porosity, Diagenesis. Tulsa: American Association of Petroleum Geologists, AAPG Memoir 77 (2003): 283–292.

> Carbonate classification systems. The Dunham classification system (top), devised in 1964, is based on rock texture and grain size. (Adapted from Akbar et al, 2000/2001, reference 1.) The Ahr classification system (bottom), published in 2005, maps pore geometry and attempts to relate stratigraphy to field-level permeability predictions. (Adapted from Ahr et al, reference 1.) Although these parameters are important for characterizing carbonate rock properties, neither classification system adequately describes key reservoir storage capacity or flow characteristics.

TS—Figure 01

Mudstone Wackestone Packstone Grainstone Boundstone Crystalline

Less than10% grains

More than10% grains

Grain supported Lacks mud,grain supported

Originalcomponentsbound together

Depositionaltexture notrecognizable

Mud supported

Contains mud, clay and fine silt-size carbonate

Original components not bound together during deposition

Depositional texture recognizable

Depositional

Hybrid 1

Hybrid 2

Hybrid 3

FractureDiageneticReduced

CompactionCementationReplacement

Diagenesis influencesbrittle behavior.

Depositional characterinfluences fractures.

Depositionalaspects dominate.

EnhancedDissolutionReplacementRecrystallization

PorositySize and shapeVugs separateVugs touching

Diageneticaspects

dominate.

InterparticleIntraparticleFenestralShelter or keystoneReef

IntraskeletalInterskeletal

Stromatactis vugsConstructed voidsDetrital infill

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42 Oilfield Review

and permeability, and invaluable fracture infor-mation. Even when rock properties are quantified for a particular well, measurement analogs beyond the near wellbore may not be valid at res-ervoir scales because of the inherent heterogene-ity and diagenetic history of the carbonates within the field.

Petrophysicists must overcome a number of difficulties when evaluating carbonates. To begin with, carbonates differ from sandstones in that they often have some type of organic origin and are more susceptible to chemical and mechanical reactions. They usually consist of skeletons and shells of animals that settled near where they lived—typically in warm, shallow marine environ-ments. Those biological structures were built from the calcium carbonate the animals extracted from seawater. The climatic conditions, the types of organisms and the manner in which they existed in their ecosystem all contribute to the reservoir heterogeneity of carbonate structures.

By contrast, the particles that make up sand-stone and mudstone deposits may travel thou-sands of kilometers to reach their final resting place. Their size, shape and sorting have much to do with the energy of the depositional environ-ment. Because carbonate sediments usually are not transported far from their source, these depo-sitional characteristics are not nearly as impor-tant. And, although most carbonate reservoirs are biogenically sourced, deepwater carbonate accumulations and precipitations that are not of biological origin have also been discovered. These can cover wide expanses and also act as hydrocarbon traps.

When the skeletal remains of biogenic car-bonates stay where the organism lived, such as coral or algal reefs, geologists refer to these accu-mulations as autochthonous.3 Lacking the inter-granular permeability of clastics, these structures usually require additional internal connectivity to be productive, most often in the form of natu-ral fractures (above). In contrast, allochthonous carbonate deposits are composed of transported shells and skeletal remains or bioclastic frag-ments eroded from reworked deposits.

Once the carbonate fragments come to rest, they eventually become cemented together, gen-erally with calcite, in a process of lithification. Because these deposits can consist of fine-grained particles or broken shell fragments, they may have clastic characteristics similar to those of sandstone. During lithification, the deposits often undergo chemical and biological diagene-sis, which produces metastable compounds that are susceptible to change (see “Diagenesis and Reservoir Quality,” page 14). After deposition, these rocks can become radically altered through diagenesis, which can enhance hydrocarbon stor-age and production capacity (porogenesis) or destroy it (poronecrosis).

The most abundant carbonate form is cal-cium carbonate, or calcite [CaCO3]. A less stable polymorph, aragonite, has the same chemical composition. Calcite is one of the more common minerals on Earth, accounting for 4% by weight of the Earth’s crust. Its chemical instability makes it susceptible to transformation into other mineral types.4 Siderite [FeCO3] can form when calcite is exposed to iron. Various other

carbonate varieties exist, each having character-istic physical properties that affect matrix den-sity and texture. The two most common carbonate reservoir rocks are limestone and dolomite. Limestone refers to the sedimentary rock form that contains calcite, although these two terms are often used interchangeably.

Determining the correct lithology—be it lime-stone, dolomite or a combination of minerals—is an important step in carbonate reservoir evalua-tion.5 Lithology establishes the matrix density, or grain density, used for computing porosity from density tools. It is also an input for other porosity measurements, such as those from thermal and epithermal neutron measurements. An accurate porosity value is a crucial input for calculating water and hydrocarbon saturations, determining total fluid volumes and estimating reserves.

> Complexity of carbonates. The carbonate matrix often tends to be complex and is composed of varying concentrations of limestone, dolomite and other minerals. Vuggy facies may make up a significant portion of carbonate reserves. Wells with connectivity through vug-to-vug contact in fracture networks generally are more prolific producers than wells with matrix permeability alone. (Core slab photograph courtesy of the Whiting Petroleum Corporation, used with permission.)

TS—Figure 02

>Matrix effects on density-porosity measurements. Density porosity is computed using a value for matrix density. If the input is unknown or incor- rect, the density-porosity measurement error can be substantial. For example, a 10% porosity limestone has a bulk density of 2.539 g/cm3. If the rock is dolomite, the porosity is 17% with that same bulk density measurement. This 70% error could be the difference between a commercial well and abandonment.

TS—Figure 03A

Calc

ulat

ed d

ensi

ty p

oros

ity, %

Limestone matrix2.71 g/cm3

Dolomite matrix2.85 g/cm3

20

15

10

5

0

φdensity = density porosityρ

matrix = matrix density, or grain densityρ

bulk = bulk density measurementρ

fluid = fluid density

ρmatrixφ density

ρbulk

ρmatrix

ρfluid

=

70% error

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Summer 2010 43

Measuring Basic PropertiesPorosity is a basic petrophysical measurement, usually obtained from well logs. It is commonly computed from bulk density data. Density poros-ity is sensitve to both the pore fluids and the matrix, especially the matrix. There are several methods available for computing porosity, and these often are affected by the fluids in the rock and the mineralogy. Depending on environmen-tal conditions and operational constraints, inte-grating these measurements plays a role in decoupling the effects of the matrix on the porosity value.

Examples of porosity measurements include those from lithology-dependent thermal neu-tron, lithology-independent neutron, acoustic, thermal neutron capture spectroscopy and nuclear magnetic resonance (NMR) tools. Neutron and NMR porosity tools are blind to the presence of gas, and NMR measurements are also blind to porosity filled with tar, bitumen, microporosity-bound water and hydrates.

In contrast to the NMR and neutron tools, bulk density tools respond to both fluid and lithology.Density porosity (φdensity) is computed using two fixed inputs, matrix density (ρmatrix) and fluid den-sity (ρfluid), and the bulk density measured by the tool (previous page, bottom). The fluid density used in calculating porosity is that of the fluid fill-ing the pores of the formation, typically 1.0 g/cm3, while the matrix density depends on the rock type. The matrix density of limestone is 2.71 g/cm3, dolo-mite is 2.85 g/cm3, siderite is 3.89 g/cm3 and sand-stone (quartz) is 2.65 g/cm3.

Uncertainty in lithology translates into large errors in computed porosity. For instance, a 10% porosity limestone formation has a measured bulk density of 2.539 g/cm3. However, a dolomite matrix could have the same measured bulk density but its porosity would be 17%. If the rock type is not cor-rectly identified, this significant discrepancy in the computed porosity—a 70% error—might be the difference between commercial viability and the decision to abandon a well.

The matrix may be a single mineral type but is often a mixture. Small concentrations of minerals, if unaccounted for, can introduce considerable error in the computed porosity. A common noncar-bonate mineral associated with limestone reser-voirs, the evaporite anhydrite, has a bulk density of 2.98 g/cm3. Dispersed within the rock matrix, a small percentage of anhydrite can significantly increase the measured bulk density. When the anhydrite is found in the form of nodules, the mea-sured porosity will be lower than the true value because logging tools average the response from both rock types (above). The formation may appear to be of poor quality, although the carbon-ate portion may, in fact, have good porosity and permeability but be masked by the anhydrite’s effects on the measurement.6

Low-porosity carbonates with heavy minerals, such as anhydrite, are emerging as major sources of bypassed hydrocarbons. Understanding the manner in which these minerals affect porosity measure-

ments and reservoir producibility is crucial for geologists who study carbonates. Core analysis often becomes a major factor in determining commerciality of a field. Logging data lack the fine resolution of core analysis, but they provide a continuous record of petrophysical properties such as porosity and lithology.

Complexity, Texture and Relative PermeabilityPerhaps the most common lithology-determina-tion method from logging data uses the photo-electric effect (PEF) measurement, which responds primarily to the minerals in the forma-tion. This measurement is routinely acquired using formation density devices, such as the Litho-Density and LWD density tools.7 Although useful in differentiating pairs of minerals among sandstone, limestone, dolomite and anhydrite, additional measurements are required when more than two minerals are present. Also, the measurement is affected by barite in drilling-mud systems, and borehole conditions such as thick mudcake and hole rugosity may render it useless.

A better method for solving complex litholo-gies and determining mineralogical concentra-tions, which may vary widely across a field depending upon the diagenetic history and fluids percolating through the reservoir, is an elemental thermal neutron capture spectroscopy measure-ment. For example, the ECS elemental capture spectroscopy and the LWD EcoScope tools offer this type of measurement.8 These tools measure the concentrations of specific elements that cor-respond to mineralogy. Various matrix properties

3. Vernon RH: A Practical Guide to Rock Microstructure. Cambridge, England: Cambridge University Press (2004): 34–37.

4. There is disagreement on how dolomite forms in nature; some scientists suggest that biogenic origins are the primary source. For more on dolomite: Al-Awadi M, Clark WJ, Moore WR, Herron M, Zhang T, Zhao W, Hurley N, Kho D, Montaron B and Sadooni F: “Dolomite: Perspectives on a Perplexing Mineral,” Oilfield Review 21, no. 3 (Autumn 2009): 32–45.

5. For more on difficulties with carbonate reservoir evaluation: Ramamoorthy R, Boyd B, Neville TJ, Seleznev N, Sun H, Flaum C and Ma J: “A New Workflow for Petrophysical and Textural Evaluation of Carbonate Reservoirs,” Petrophysics 51, no. 1 (February 2010): 17–31.

>Mineralogical effects. Anhydrite is just one of many minerals found within carbonate reservoir rocks. The manner in which this mineral is dispersed may affect fluid flow in the reservoir. It may also impact the porosity measurement. In the case of anhydrite nodules, the porosity of the reservoir rocks tends to be underestimated and fluid flow is not greatly affected (core photograph, right). If the anhydrite is dispersed within the pore structure (micrograph, left), the porosity measurement will be reduced, as will fluid flow. (Adapted from Ramamoorthy et al, reference 5.)

TS—Figure 04

Pore-filling anhydrite

Anhydrite nodule

6. Ramamoorthy et al, reference 5.7. The PEF is a log of photoelectric absorption (Pe) properties

of the rock matrix that is acquired along with formation density measurements. Common minerals encountered in oil and gas wells have specific Pe values: sandstone (1.9), dolomite (3.1), limestone (5.1) and anhydrite (5.0).

8. Japan Oil, Gas and Metals National Corporation (JOGMEC), formerly Japan National Oil Corporation (JNOC), and Schlumberger collaborated on a research project to develop LWD technology that reduces the need for traditional chemical sources. Designed around the pulsed neutron generator (PNG), EcoScope service uses technology that resulted from this collaboration. The PNG and the comprehensive suite of measurements in a single collar are key components of the EcoScope service that deliver game-changing LWD technology.

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can also be computed from the yields, including grain density.9 Grain density represents an effec-tive matrix density and varies according to the elements present in the formation. It yields more- accurate density porosity than when computed using a fixed-value matrix density.

Texture and pore geometry are also important properties for identifying reservoir-quality rock because knowledge of correct mineralogy and porosity measurement alone is not sufficient to infer flow characteristics in carbonate reservoirs. In fact, some experts believe that characteriza-tion of pore geometry is the most important com-ponent in carbonate evaluation.10 Complex pore shapes and sizes often result from reservoir depo-sition and the ensuing processes of dissolution, precipitation and recrystallization. Although time-consuming, core analysis can reliably iden-tify and quantify pore geometry. The standard resistivity and porosity measurements of a triple-combo logging suite often do not respond to changes in pore size and texture. NMR data, how-ever, have been shown to identify changes in pore size distribution not detectable by these conven-tional logs (left).

To better evaluate reservoir rock quality using logging data, experts developed a technique for characterizing carbonate pore geometry by parti-tioning the total porosity measurement into three classes of pore spaces based on size—micro- (less than 0.5 microns), meso- (0.5 to 5 microns) and macroporosity (larger than 5 microns). From these partitions, reservoir quality and fluid-flow properties are inferred.11 Partitioning of forma-tion porosity by pore size uses specific ranges of transverse relaxation times, T2, from NMR data.12 Core data are often used to refine T2 measure-ment ranges (left).

Another partitioning method maps relative pore geometry into eight rock classes (next page, bottom left).13 The resulting ternary diagram was first developed through systematic analysis of texture-sensitive borehole logs, which included NMR data, borehole images, full-waveform acous-tic logs and dielectric data.14 A similar ternary diagram has been derived from mercury injection capillary pressure (MICP) tests on core.

For macroporosity evaluation, geophysicists have recently begun to use acoustic data, such as those from the Sonic Scanner tool, to estimate the fraction of vuggy porosity. One application of these data is to fine-tune the cementation expo-nent, m, in Archie’s water saturation equation. Vugs tend to increase the cementation exponent, while large intergranular pores do not. Use of macroporosity fractions from NMR data alone

> Pore size and geometry. Measurements from NMR logging tools are more sensitive to pore size and geometry than are resistivity and other porosity measurements. The gamma ray log (Track 1), resistivity logs (Track 2) and porosity measurements (Track 3) are consistent throughout the interval shown. The NMR data (Track 4) indicate a large increase in pore size above X,040 ft that is not seen in the other measurements. (Adapted from Ramamoorthy et al, reference 5.)

T2 Distributions

Depth,ft

X,050

X,000

0 100gAPI

Gamma Ray6 16in.

Caliper

6 16in.

Bit Size

0.1 1,000ohm.m

Array 1

Array 2

Array 3

Array 4

Array 5

Rxo

Resistivity

45 –15%

Neutron Porosity

45 –15%

Array Porosity

3 13

PEF1.95 2.95g/cm3

Bulk Density

0.3 6,000ms

T2 Log Mean

>NMR porosity partitioning. When NMR logging tools were introduced to the oil industry, the T2 distributions were scaled as pore sizes. For a number of reasons, this practice was abandoned. However, the concept works fairly well for carbonates. Pore sizes are determined according to a range of T2 distributions, and then the porosity is partitioned into macro-, meso- and microporosity based on these measurements. The longest T2 distributions correspond to macroporosity, large pores and vugs. The shortest T2 distributions respond to microporosity. Oil migrating into water-filled rock displaces water in macro- and mesopores first. Micropores generally remain water filled.

TS—Figure 07

Total porosity

Oil in place

0.5microns

5microns

Mesoporosity MacroporosityMicroporosity

Porositybelow short

T2 cutoffNMR T2

response

Porosityabove long T2 cutoff

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Summer 2010 45

can result in elevated estimates of m because the measurement is based on pore size, not shape. Combining vuggy porosity estimates from full-waveform acoustic data improves log-derived estimations of the m exponent.

NMR data are also used to compute permea-bility. The technique evolved from empirically derived relationships, which work well in sand-stones but are not always relevant in carbonates because the pores may not be connected. Relative permeabilities and fractional flow in hydrocarbon zones may, however, be derived from array resistivity log data when the well is drilled with water-base mud.15 The invading mud filtrate acts as an uncontrolled two-phase flow experiment that can be analyzed in a manner similar to relative permeability measurements conducted on core.

This mud-filtrate invasion method not only provides information about in situ fractional flow and relative permeabilities, it also improves the accuracy of formation resistivity measurements and water saturation estimates. The processing involves forward modeling based on relative

permeability parameterization, radial invasion models, petrophysical models and tool response to specific conditions. The inputs required for computing water saturations using Archie’s equa-tion—formation water and bulk formation resis-tivities—are more accurate when obtained using this method, as are the ultimate computed fluid volumes. Even so, log analysts have discovered that Archie’s equation may not be as reliable for characterizing fluids in carbonate reservoirs as it is in sandstones.

What’s Wrong with Archie?In 1942 Gus Archie laid the foundation for mod-ern log interpretation by introducing a relation-ship linking water resistivity, formation porosity and formation resistivity to fluid saturation (right). Variables in the equation—a, m and n—are empirically fit based on reservoir characteris-tics. In the absence of specific data they are generally assumed to equal 1, 2 and 2, respec-tively.16 Assumptions in the formula—morphol-ogy of the pore space, connectivity of the pores and wettability of the rock—are best suited to

> A ternary diagram based on pore size. Carbonate pore geometry and size are inputs to this ternary diagram, which indicates reservoir quality. On the lower left side of the triangle, permeability is a function of grain size. For the upper section, permeability is controlled by the volume of macropores. On the lower right, the permeability is a function of both grain and pore size.

TS—Figure 06

k = 0.35 φ2 ( T2LM××

)2

Carbonate rocks with intergranular

T2LM is thelogarithmic mean ofthe T2 measurement.

porosity (no macroporosity)

Permeability, k, is controlled byporosity and the average pore(grain) size. k = 1.0 Vmacro /(Vmeso + Vmicro) ][ 2

Carbonate rocks with abundantmacroporosityWepore throats)

ll-connected pores (large

Permeability is controlled byporosity and the volume ofmacroporosity (Vmacro).

100%microporosity

100%mesoporosity

Carbonate Pore System Classes and Permeability100%

macroporosity

ρφ2

> Archie’s water saturation equation (bottom). Porosity and Rt are log-derived measurements. Rw is either derived from water salinity or measured from produced water and converted to downhole temperature. Variables a, m and n are empirically fit based on reservoir characteristics. They are assumed equal to 1, 2 and 2, respectively, in the absence of specific data. A sensitivity analysis (top) demonstrates the effects of varying m and n on computed water saturation. First, n is set to 2 and m is varied from 2.3 to 1.7 (Track 1). Next, m is fixed and n is varied from 2.5 to 1.0 (Track 2). The baseline water saturation curve using default inputs for m = n = 2 is presented in both tracks (red curve). (Adapted from Griffiths et al, reference 17.)

TS—Figure 06A

Sw = Archie’s water saturation

Rw = resistivity of formation water

R t = true formation resistivity

a = formation-factor multiplier

= porosity

m = cementation exponent

n = saturation exponent

%

n = 2,m = 2.3 to 1.7

100 0 %

m = 2,n = 2.5 to 1.0

100 0

Water Saturation Water Saturation

Sφ

φ

RRa

m=

t

ww n

9. For a thorough review of neutron capture spectroscopy: Barson D, Christensen R, Decoster E, Grau J, Herron M, Herron S, Guru UK, Jordán M, Maher TM, Rylander E and White J: “Spectroscopy: The Key to Rapid, Reliable Petrophysical Answers,” Oilfield Review 17, no. 2 (Summer 2005): 14–33.

10. Archie GE: “Classification of Carbonate Reservoir Rocks and Petrophysical Considerations,” AAPG Bulletin 36, no. 2 (1952): 278–298.

11. Hassall JK, Ferraris P, Al-Raisi M, Hurley JF, Boyd A and Allen DF: “Comparison of Permeability Predictors from NMR, Formation Image and Other Logs in a Carbonate Reservoir,” paper SPE 88683, presented at the Abu Dhabi

International Petroleum Conference and Exhibition, Abu Dhabi, UAE, October 10–13, 2004.

12. In NMR logging, transverse relaxation time, T2, results from interactions of hydrogen atoms with their surroundings, including effects of bulk fluids, pore surfaces and diffusion in magnetic field gradients. Short T2 times correspond to small pores, and longer T2 times correspond to larger pores.

13. Hassall et al, reference 11.14. Ramamoorthy et al, reference 5.15. For more on this technique: Ramakrishnan TS,

Al-Khalifa J, Al-Waheed HH and Cao Minh C: “Producibility Estimation from Array-Induction Logs

and Comparison with Measurements—A Case Study,” Transactions of the SPWLA 38th Annual Logging Symposium, Houston, June 15–18, 1997, paper X.

16. The a constant, a tortuosity or consolidation factor, was not in Archie’s original equation but was added later as a means of correcting for saturation in known water-filled reservoir rocks. For more on this subject: Archie GE: “The Electrical Resistivity Log as an Aid in Determining Some Reservoir Characteristics,” Petroleum Transactions of AIME 146 (1942): 54–62.

Winsauer WO, Shearin HM, Masson PH and Williams M: “Resistivity of Brine Saturated Sands in Relation to Pore Geometry,” AAPG Bulletin 36, no. 2 (1952): 253–277.

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siliciclastic rocks.17 Although most water satura-tion methods utilize some form of Archie’s equa-tion, it is generally recognized that there are problems with this approach when applied to carbonates. Even Gus Archie stated that he doubted the applicability of his equation in car-bonate evaluation.18

In addition, the complex nature of carbonates makes determination of the a, m and n variables difficult, and these values may change rapidly throughout the reservoir.19 Other problems with using Archie’s saturation equation in carbonates include matrix complexity, pore size heterogene-ity, pore shape and distribution, variability in for-mation water salinity and uncertainty in the true formation resistivity measurement.

The process of filling the reservoir creates some of the difficulties encountered when using Archie’s water saturation equation: Water fills the pores initially and then hydrocarbons enter, charging the complex carbonate structure. The macropores fill first, because they have the low-est capillary entry pressure. A proportion of the mesopores fill next and, because of capillary pressure, micropores may remain water filled. As a result of the basic nature of carbonate grain surfaces, there is an affinity for crude oil, which typically contains acidic components. Hence, the pores that fill with oil may become oil wet, while micropores that never fill with oil remain water wet. This results in a mixed-wettability rock.

Moved by natural or injected water sweeping through producing fields or by filtrate during drilling, reservoir fluids are displaced in the larg-est pores first. Because of the altered wettability

in the rock, these pores present the least resis-tance to the ingress of the fluids. Fluid capillary effects and differences between the original charging pressure and reservoir pressure during production may result in some of the mesopores remaining oil filled even as the macro- and micro-pores are water filled. This creates a complex fluid distribution inside the pore network. Thus, Archie parameters are different for the invaded rock of the near-wellbore area than for the unin-vaded zones of the same rock (above).

The complex wettability of carbonates makes use of Archie’s saturation equation problematic as well. Unlike sandstone reservoirs that are usu-ally strongly water wet, most carbonate reservoir rocks have some degree of moderate oil-wet char-acter. Preferentially oil-wet surfaces, located on the walls of meso- and macropores, have been in contact with oil. This reduces the connectivity of the water phase in the porous rock and contrib-utes to an increase in the resistivity compared with the value predicted by Archie’s equation.

On the other hand, micritic grains—tightly packed micron-size calcite crystals with sub-micron pores—are fully water saturated and water wet and dramatically enhance the connec-tivity of water in the medium. The effect of micrite counteracts the effect of oil-wetness on the rock’s electrical properties. Carbonate rocks with a large volume fraction of micrite may have a resistivity similar to that of shaly sandstone rocks. Carbonate rocks with little or no micritic content, such as dolomite, may have a pro-nounced opposite response typical of oil-wet rocks. These resistivity behaviors can be modeled by the connectivity equation.20

In Archie’s saturation equation, the term for formation water, Rw, assumes a simple fluid distri-bution with a single value of formation water resis-tivity. Complex fluid distributions, such as mixed filtrate or injection waters, are a departure from

> Carbonate reservoir filling and resistivity measurements. Water (blue) originally fills the pore spaces of carbonate reservoirs (left). As oil (green) migrates into the rock, large pores fill first. If there is no connectivity, some pores may remain water filled (center). Because resistivity tools measure through a path of least resistance (red line), the current may bypass oil-filled pores (right), which will increase the measured resistivity. Thus the resistivity values may be substantially lower than expected and not be representative of the true bulk resistivity.

TS—Figure 08

Φ density, is the density porosity

ρ matrix, is the matrix density, or grain density

ρ bulk, is the tool measurement

ρ fluid, is the fluid density

ρmatrixΦ density

ρbulk

ρmatrix

ρfluid

=

Micropores

MesoporesMacropores Water-filled vug Path of least resistance

> Sigma equation for water saturation. Standard values for the matrix sigma, Σgrain, are shown (top), although the measurement can be refined with spectroscopy data. Values for Σwater can be calculated using fluid salinity, computed from log responses or directly measured from produced water samples. This equation (bottom) provides a water saturation value that is not based on resistivity measurements.

TS—Figure 09

Lithology

Sandstone = 4.3

Dolomite = 4.7

Calcite = 7.1

Anhydrite = 12

Σ, cu 0 5 10 15 20 25 30 35

Clays

40 45 50

Fluid Gas Oil Fresh water Increasing salinity

Φ density, is the density porosity

ρ matrix, is the matrix density, or grain density

ρ bulk, is the tool measurement

ρ fluid, is the fluid density

Sw = formation water saturation

φ = formation porosity

Σ bulk = measured formation capture cross section

Σ water = capture cross section of the water

Σ grain = formation grain capture cross section

Σ HC = hydrocarbon capture cross section

ρmatrixΦ density

ρbulk

ρmatrix

ρfluid

=Σbulk Σgrain

w =( ) Σgrain ΣHC( )

Σwater ΣHC( )φ

φS ×

×

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Summer 2010 47

the model. The true resistivity in the reservoir, Rt, can be difficult to measure as well. Water-filled microporosity and mesoporosity provide paths of lower resistance for the sensor current. Thus the average bulk resistivity measured in the formation is significantly lower than that in rocks with identi-cal porosity and fluid saturations but with uni-modal pore-throat or pore-body size distributions. These occurrences, referred to as low-resistivity–pay formations, have led to underestimated hydro-carbon reserves and bypassed hydrocarbons.

For these and other reasons, Archie’s satura-tion equation is unlikely to be accurate for car-bonates without making empirical adjustments to the input variables. An alternative to Archie’s equation derives saturation from the macro-scopic thermal neutron capture cross section measurement, or sigma (Σ, measured in capture units, cu), which has been used for cased hole evaluation for many years. A pulsed-neutron gen-erator (PNG) emits high-energy neutrons that interact with the nuclei of the elements present in the surrounding formation. Of the elements generally found in the reservoir, chloride ions [Cl–], primarily found in salt water, have the greatest neutron capture capacity, also referred to as capture cross section. The rate of neutron capture is predominately a function of chloride concentration, which can be related to the vol-ume and salinity of the formation water. Hydrocarbons have a low capture capacity, and as long as there is sufficient salinity in the forma-tion water to produce a usable sigma contrast between hydrocarbons and water, sigma can be used to compute water saturation.

Inputs for computing water saturation using sigma are porosity and macroscopic capture cross section for formation matrix (Σgrain), formation water (Σwater), expected hydrocarbons in place (ΣHC) and the sigma measured by the tool (Σbulk) (previous page, bottom). If the lithology is known, matrix sigma can be input as a constant, or it can be derived from the elemental thermal neutron capture spectroscopy measurement in a manner similar to determining grain density for porosity calculations. The value of Σwater can be measured directly, estimated from downhole measurements or calculated from the salinity of produced sam-ples. Finally, ΣHC, a constant used in the satura-tion equation for the hydrocarbon type, is derived from expected fluid properties at downhole tem-perature and pressure.

The depth of investigation of the sigma measurement is quite shallow compared with that of resistivity measurements. Thus the ability to characterize the uninvaded portion of the res-ervoir may be significantly hindered because

mud filtrate invades the near-wellbore zone dur-ing the drilling process. The sigma measurement may respond primarily to the filtrate. As a conse-quence, wireline sigma measurements acquired in open hole have not proved useful for evaluat-ing water saturation in the virgin zone. One exception to this occurs when the invaded and uninvaded zones remain similar, such as when drilling in oil-bearing formations at irreducible water saturation with oil-base mud. In this case the time of the measurement does not matter, but the assertion that the formation is at irreducible water saturation must be validated.

Cased hole sigma logs have proved more beneficial than openhole logs because they are acquired after the filtrate has dissipated. Even so, the measurement may be degraded by the effects of casing, cement and residual fluids. This has led to differences between saturations mea-sured with cased hole tools and those derived from openhole logs.

An alternative to openhole and cased hole sigma measurements from wireline tools is sigma measured using an LWD tool. Depending on such

factors as drilling rate of penetration (ROP), for-mation porosity, formation permeability, mud properties, mud pressure overbalance and the elapsed time between the first drilling in the for-mation and the time of acquiring the sigma mea-surement, the invaded zone may not extend into the region of the measurement’s depth of investi-gation. Acquiring data close behind the drill bit and prior to invasion overcomes many of the limi-tations of sigma acquisition using wireline meth-ods. This capability has been available for several years with the EcoScope tool, a multifunction LWD service that combines resistivity sensors with a PNG for sigma and sourceless thermal neu-tron porosity logging (above). The EcoScope tool

17. Griffiths R, Carnegie A, Gyllensten A, Ribeiro MT, Prasodjo A and Sallam Y: “Evaluation of Low Resistivity Pay in Carbonates—A Breakthrough,” Transactions of the SPWLA 47th Annual Logging Symposium, Veracruz, Mexico, June 4–7, 2006, paper E.

18. Griffiths et al, reference 17.19. Griffiths et al, reference 17. 20. For more on wettability and carbonates, especially

modeling of resistivity: Montaron B: “Connectivity Theory—A New Approach to Modeling Non-Archie Rocks,” Transactions of the SPWLA 49th Annual Logging Symposium, Edinburgh, Scotland, May 25–28, 2008, paper GGGG.

> EcoScope LWD tool. The EcoScope tool incorporates resistivity, neutron porosity, sigma and neutron capture spectroscopy sensors into a single compact device. Wireline and LWD tools generally use chemical sources for neutron porosity and neutron capture spectroscopy measurements. The EcoScope tool generates neutrons with a pulsed-neutron generator that operates only when mud is being pumped through the tool.

Phase resistivity

TS—Figure 10

Attenuation resistivity

Sigma, sourceless neutronporosity, spectroscopy andneutron-gamma density

Azimuthal densityand PEF

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48 Oilfield Review

is considered sourceless because once power—which is generated from mud flowing through the tool—is no longer applied to the PNG, it ceases to emit neutrons. Conversely, chemical sources are “always on.”

The neutron output from the PNG also makes thermal neutron capture spectroscopy measure-ments possible. Similar to the measurements from the wireline ECS tool, the EcoScope spec-trometry service delivers elemental yields of sili-con [Si], calcium [Ca], iron [Fe], sulfur [S],

titanium [Ti], gadolinium [Gd], potassium [K], hydrogen [H] and chlorine [Cl]. Although the EcoScope tool was not able to differentiate lime-stone from dolomite in the past, the tool response was recently recharacterized to include a magne-sium [Mg] measurement (below). The ability to measure Mg is fundamental for distinguishing dolomite from limestone. In barite-weighted mud systems, this becomes a crucial measurement for determining formation lithology because the PEF

measurement from a Litho-Density tool is ren-dered unusable by the effects of the barite. In complex mineralogy the spectroscopy measure-ment helps identify mineral constituents and pro-vides an effective matrix density, or grain density, for more-accurate density-porosity computations.

Complex Middle East Carbonate Recently the EcoScope tool was run in an offshore Abu Dhabi carbonate field.21 Production from this field began in 1968 from Lower Cretaceous, Upper Jurassic, Upper Permian and Lower Triassic for-mations. In 2006 Total decided to drill and develop the Late Triassic (Gulailah) and Lower Jurassic (Hamlah) Formations, which had not been previ-ously produced.

The Hamlah reservoir is 50 m [164 ft] thick and comprises two intervals separated by shale. The lower interval is a micro- to very fine-grained crystalline dolomite interbedded with limestone streaks. The upper interval grades between lime-stone, wackestone to packstone, with some grain-stone and dolomite. Porosity ranges from 6% to 8%, and permeability ranges from very low to low.

The Gulailah reservoir is 250 m [820 ft] thick, with alternating dolomitic and anhydritic beds. The dolomites are sucrosic to finely crystalline, anhydritic and occasionally argillaceous. Porosity ranges from 8% to 13% and permeability is low to very low.

Deviated wells were drilled using 1.35-g/cm3

[11.3-lbm/galUS] barite-weighted mud systems. This barite significantly degraded the PEF mea-surement. The EcoScope tool’s spectroscopy measurement was able to accurately distinguish calcite from dolomite and provide the matrix grain density.

Another common complication encountered in evaluating deviated wells—especially in carbon-ates—is resistivity anomalies caused by shoulder-bed effects. These arise when the measurement volume includes regions with large conductivity contrasts. Electromagnetic averaging and charge buildup along the interface between layers result in polarization horns, seen as anomalous spikes in the resistivity data (next page).22

Although shoulder-bed effects are generally small in vertical wells, for deviated and horizon-tal wells these effects may be prominent in long intervals as wells approach, intersect and depart from layer boundaries. Resistivities affected by shoulder beds can produce misleadingly high hydrocarbon saturations when calculated using Archie’s saturation equation.

> Refining lithology determination. Standard SpectroLith processing (left) cannot distinguish calcite from dolomite in the absence of a PEF or magnesium measurement and assumes that all calcium is associated with calcite. When lithology is computed using the PEF measurement from a Litho-Density tool, the software is able to distinguish dolomite from calcite (center), but the PEF measurement can be affected by barite in the drilling fluids and by hole conditions. The excessive anhydrite shown in the center track is attributed to these effects. If more than two minerals are present, the PEF measurement is less accurate. Spectroscopy that includes a magnesium measurement (right) distinguishes dolomite from calcite and is not affected by hole conditions and fluid properties. Other minerals can be accurately quantified as well.

Carbonate

Pyrite

Anhydrite-Gypsum

Clay

Quartz-Feldspar-Mica

Illite

Bound Water

Quartz

Anhydrite

Calcite

Dolomite

Illite

Bound Water

Quartz

Anhydrite

Calcite

Dolomite

Standard SpectroLith Calcite-Dolomitefrom PEFProcessing

Calcite-Dolomite fromEnhanced Spectroscopy

Oilfield ReviewAutumn 10CleanPhase Fig. 11ORAUT10-CLNPSE Fig. 11

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Summer 2010 49

The superiority of sigma-based saturation measurements over conventional methods is compromised in the presence of significant mud- filtrate invasion. Resistivity-response modeling has shown that invasion less than 5 cm [2 in.] has

negligible effects on the sigma measurement. Generally, because the measurement is taken so close to the bit, the formation does not have time to become significantly invaded before the EcoScope tool acquires data. The tool’s resistivity sensor array, collocated with the sigma measure-

ment, can determine the degree of invasion in the area sampled.

21. Griffiths R and Poirier-Coutansais X: “Complex Carbonate Reservoir Evaluation—A Logging While Drilling Field Example,” paper AA, presented at the SPWLA Regional Symposium, Abu Dhabi, UAE, April 16–18, 2007.

22. Griffiths and Poirier-Coutansais, reference 21.

> Shoulder-bed effects on LWD resistivity measurements. Averaging of resistivity measurements affects the output at bed boundaries. In wells drilled nearly perpendicular to the layering (top left), these effects tend to be localized as the tool crosses a resistivity interface. Horizontal wells may cross multiple zones with large resistivity contrasts (top right). In this situation, charges accumulate at the interface and induce a polarization horn, or spikes—which are dependent on the depth of investigation—that are not representative of the actual resistivity (middle). If not accounted for during interpretation, the elevated resistivities produce misleadingly high hydrocarbon saturations using Archie’s saturation equation. The sigma measurement (bottom) does not suffer from the polarization effect, permitting a more accurate evaluation of the hydrocarbon saturation in high-angle wells.

TS—Figure 12

1 ohm.m

50 ohm.mRe

sist

ivity

, ohm

.m

5,000 5,010 5,020Distance from boundary, ft

5,030 5,040

1,000

100

10

1

1 ohm.m 50 ohm.m

Sigm

a, c

u

5,000 5,010 5,020Distance from boundary, ft

5,030 5,040

1,000

100

10

1

1 ohm.m 50 ohm.m

– –+ +

1 ohm.m

50 ohm.m

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50 Oilfield Review

In the Total well, the preinvasion sigma from the EcoScope tool provided a valid water satura-tion measurement independent of formation resistivity. As an added benefit, petrophysicists were able to determine appropriate inputs to Archie’s water saturation equation to match the sigma-based measurement. Because carbonate reservoirs often have unknown Rw values, simul-taneously solving for water salinity provided a realistic Rw and Σwater output that satisfied both equations (above).

Sum Greater than PartsThe EcoScope approach provides answers about fluid saturations in carbonates, but a preinvasion sigma measurement is often unavailable. Recognizing the challenges in carbonate

evaluation, Schlumberger scientists devised a workflow for petrophysical and textural evalua-tion that integrates standard wireline logging suites with recently introduced measurements. Several independent research efforts focusing on discrete aspects of carbonate evaluation are com-bined using this systematic methodology. The workflow evolved into the Carbonate Advisor soft-ware program (next page, top left). Each step in the workflow provides a piece of the puzzle and facilitates subsequent steps.

Petrophysicists applied this methodology to a Cretaceous Middle East carbonate well that had a comprehensive suite of wireline logs. The log-ging program included array resistivity (both induction and laterolog), gamma ray, density, thermal and epithermal neutron, NMR, full-wave-

form acoustic, neutron capture spectroscopy and microresistivity imaging tools.

The analysis hierarchy began with lithology and mineralogy determinations from fluid- and matrix-sensitive data, including NMR informa-tion, density and neutron porosity logs, PEF logs and neutron capture spectroscopy data. The pet-rophysicist can emphasize the importance of a particular measurement based on its relevance and the borehole environment to obtain a simul-taneous solution that includes input from all measurements.23 In this case the mineralogy con-sists predominantly of calcite with small amounts of dolomite. Siliciclastic material and anhydrite were also observed (next page, top right). Elemental thermal neutron capture spectros-copy data quantified the dolomite, anhydrite,

> Improved Archie’s equation and sigma saturation measurements. Apparent formation salinity is computed assuming the formation is 100% water saturated (Tracks 3 and 5, green curves). Apparent salinity from the spectroscopy chlorine/hydrogen (Cl/H) ratio measurement (Tracks 3 and 5, blue curve) is presented for comparison. Archie saturation is calculated using n and m exponents set to 2 and an Rw based on the assumed salinity corrected for downhole conditions (Tracks 4 and 6, blue curve). Sigma-based saturations (red curve) are computed using two different water salinities: 250 and 150 parts per thousand (ppt). The red lines in Tracks 3 and 5 indicate the salinity input used for each analysis. The analysis using 250-ppt salinity water (Tracks 3 and 4), which was the original assumption, exhibits a large separation between the two saturation solutions. Also, the SpectroLith apparent salinity (blue curve) does not match the salinity used in the analysis (red line). For the 150-ppt salinity analysis (Tracks 5 and 6), the SpectroLith apparent-salinity curve (blue) tracks the salinity value used in the analysis (red line), and both saturation methods are in much closer agreement (Track 6). This simultaneous solution yields a more reliable saturation measurement and a more reasonable choice for formation-fluid salinity. Note the lack of separation between deep and shallow resistivities (Track 1) indicating shallow invasion and acceptable sigma measurement. Neutron and density porosities, adjusted for matrix lithology from spectroscopy data, are also presented (Track 2). (Adapted from Griffiths and Poirier-Coutansais, reference 21.)

Oilfield ReviewAutumn 10CleanPhase Fig. 13ORAUT10-CLNPSE Fig. 13

Resistivity Matrix-Adjusted Porosity

Neutron Porosity

Density Porosity

Total Porosity

0.2 2,000ohm.m 50 0% 400 ppt 4

SpectroLith Apparent Salinity

Sigma Apparent Salinity

250-ppt Salinitya = 1, m = n = 2

100 % 0

Water Saturation(Sigma)

Water Saturation(Archie)

400 ppt 4

SpectroLith Apparent Salinity

Sigma Apparent Salinity

150-ppt Salinitya = 1, m = n = 2

100 % 0

Water Saturation(Sigma)

Water Saturation(Archie)

50 0%

50 0%

400 ppt 4100 % 0 100 % 0

Free WaterIrreducible WaterClay-Bound Water

Free WaterIrreducible Water

40-in. Blended LWD Tool

40-in. 2-MHz Phase Shift

28-in. 2-MHz Phase Shift

16-in. 2-MHz Phase Shift400 ppt 4

Clay-Bound Water

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Summer 2010 51

quartz and clay (illite) volumes to generate an effective grain density, allowing an accurate porosity to be obtained.

The lithology-corrected porosity was next par-titioned into pore geometry components based on NMR data, which were fine-tuned with bore-hole image and full-waveform acoustic data. In contrast to the lithology and mineralogy, the pore geometry was highly variable, with zones contain-ing significant amounts of macroporosity inter-spersed with zones dominated by mesoporosity and lesser amounts of microporosity (left).

> Integrated carbonate solution. This flowchart shows the workflow sequence for analyzing carbonate reservoirs using Carbonate Advisor software.

TS—Figure 14

Density, PEF, neutron,NMR, spectroscopy

NMR, borehole images,acoustic data

Formation testers

NMR pore sizetransforms

Resistivity, sigma,dielectrics, 3D NMR data

Array resistivities,formation tester data

Lithology, porosity,fluid type

Input Data Outputs

Porosity partitioning

Permeability

Petrophysicalrock types

Inte

grat

ed c

arbo

nate

eval

uatio

n

Capillary pressures

Fluid saturations

Fractional flow

> Lithology defined by the ECS tool. The measurement principle for neutron capture spectroscopy is the same for both the ECS and the EcoScope tools; the difference is the neutron source. The ECS sonde has a chemical source and the EcoScope tool uses a pulsed-neutron generator with a higher neutron output. Traditional methods for determining lithology use PEF data from a Litho-Density tool (left). This method is best suited for two-mineral models. By adding elemental yield data from the ECS tool (right), the lithology can be refined, providing a more accurate density-porosity measurement because the grain density reflects the true mineralogy. The porosity difference between using a fixed limestone matrix density value and an effective grain density computed from ECS mineralogy is presented (Track 2, orange shading). (Adapted with permission of the SPWLA from Ramamoorthy et al, reference 5.)

Anhydrite

Calcite

Dolomite

Illite

Dolomite

Calcite

Anhydrite

Quartz

Bound Water

Porosity Correction

Oilfield ReviewAutumn 10CleanPhase Fig. 15ORAUT10-CLNPSE Fig. 15

23. Ramamoorthy et al, reference 5.

> Porosity partitioning of NMR data. The distribution of T2 transverse relaxation time data (Track 1) from the NMR tool is partitioned based on cutoffs that can be refined from core analysis. In this example volumes computed from distributions to the left of the red line (Track 1) represent microporosity, which correspond to the blue shaded volume in Track 2. Microporosity measurements from core are plotted along with the microporosity volume for confirmation. The area between the red and blue lines in Track 1 is mesoporosity, corresponding to the green shading in Track 2. The macroporosity (red shading) is associated with remaining porosity (Track 1, right of the blue line). Permeability from core data is plotted with permeability computed from NMR data (Track 3). The free-fluid volume computed from NMR data can be similarly partitioned (Track 4). Fluid volume to the right of the cutoff (blue line) is associated with mesoporosity, and the volume to the left is macroporosity. Core data points agree with computed data. (Adapted from Ramamoorthy et al, reference 5.)

Depth,ft 0.5 50,000ms

50 % 0

Total Porosity

50 % 0

Core Microporosity

0.5 50,000ms

X,500

X,600

0.1 10,000mD

Core Permeability

0.1 10,000mD

Computed Permeability

30 % 0

Core Macroporosity30 % 0

Macroporosity Cutoff30 % 0

Free Fluid, NMR

Microporosity

Mesoporosity

MacroporosityT2 Distributions

T2 Cutoff Short

T2 Cutoff Long

Oilfield ReviewAutumn 10CleanPhase Fig. 16ORAUT10-CLNPSE Fig. 16

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52 Oilfield Review

The partitioned porosity from NMR data had good correlation with data from MICP test results. Analysts next used the partitioned poros-ity to estimate permeability. These log-derived values compare well with minipermeameter probe measurements made on core plugs.

Relative permeability and fluid saturations were computed using both array induction and array laterolog resistivity measurements. Because of the high salinity of the borehole fluid, the induc-tion measurement was unreliable at high resistivi-

ties in the main hydrocarbon section. The laterolog data are preferred in these zones.

Drainage capillary pressures were also com-puted based on NMR data transforms.24 Because the NMR data provide pore size from T2 distribu-tions, assuming bulk and diffusion effects are minimal, by integrating the T2 distribution, a cap-illary pressure versus saturation relationship can be developed. To convert T2 data to capillary pres-sure, a small calibration constant is required. This constant is obtained by comparing the NMR data with MICP measurements taken from simi-lar core samples. Using the Carbonate Advisor

program, the analyst manually determines the constant by comparing MICP entry pressures with those computed from NMR log data.

The integrated approach of the Carbonate Advisor software provides comprehensive evalua-tion of key properties that describe reservoir storage capacity and flow characteristics (above). The software follows a set workflow, but through-out the process the petrophysicist has interactive control over how data are input, a particularly useful feature when measurement conditions may be less than optimal.

> Integrated output. Shown is the final product from the Carbonate Advisor program. These outputs provide an integrated and comprehensive evaluation of the key properties that describe a reservoir’s storage and flow capacity. The petrophysicist may weight the data from specific tools and choose between tools (Depth track, AIT array induction imager tool, green; and HRLA high-resolution laterolog array, gold). Complex lithology and fluid volumes (Track 1) are shown along with a moved-hydrocarbon analysis (orange) from microresistivity data. Fluid-flow models are constructed from resistivity data (Track 2). Porosity from NMR data (Track 3) are partitioned and the results graphically displayed (Track 4). A full ternary analysis (Track 5)

is useful for identifying better quality reservoir rock. Drainage capillary pressures are computed from NMR pore geometry data, adjusted to match MICP data when available, and then plotted with water saturation (Track 6). The dark-blue shading indicates the pore space that can become oil filled at low capillary pressure. The shading transitions from blue to red, corresponding to successively higher capillary pressures required to fill additional pore volumes. Thus the layer around X,600, with more dark-blue shading than the mostly red and yellow layer around X,500, represents better quality rock. (Adapted from Ramamoorthy et al, reference 5.)

AIT ToolMoved Hydrocarbon

HydrocarbonWater

Depth,ft

Pyrite

QuartzAnhydrite

CalciteDolomite

HRLA Tool

Siderite

KaoliniteChlorite

Illite (dry)

Montmorillonite

Lithology

Contributing Flow

0 % 100

T2 Distributions50 0%

Core Porosity

Total Porosity

50 0%

Microporosity

MacroporosityMesoporosity

NMR Porosity Partition

Computed Permeability

0.1 10,000mD

Core Permeability0.1 10,000mD

MicroporosityMicro–mesoporosityMicro–macroporosityMeso–microporosityMacro–microporosity

MesoporosityMacro–mesoporosity

MacroporosityTernary Porosity Partition

X,400

X,500

X,600

Capillary PressureMin Max

100%0Water Saturation

Oilfield ReviewAutumn 10CleanPhase Fig. 17ORAUT10-CLNPSE Fig. 17

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Searching Above GroundApproaches discussed so far apply to data acquired downhole. Because of the heterogeneity of carbon-ate reservoirs, the shallow depth of investigation of most logging tools may limit their use for opti-mizing well positioning. For instance, fracture ori-entation obtained from imaging tools can be influenced by local effects and may not reflect the predominant trend in the reservoir. However, new developments in seismic technology are providing operators with assistance in detecting fracture swarms within a reservoir and this knowledge can be used to optimize well locations.

Three-dimensional surface seismic surveys offer an expanded view of reservoir heterogene-ity, extending over the entire field. Variations in the reservoir properties such as porosity, clay content and water saturation can all be charac-terized using seismic measurements, although their resolution and detection level are limited

by the seismic wavelengths used, survey design and other factors such as near-surface–gener-ated noise. Recent developments in seismic acquisition tools and processing techniques have increased the usable bandwidth and signal-to-noise ratio such that higher resolution data with enhanced signal fidelity are now obtainable. Consequently, geoscientists are able to charac-terize in finer detail the heterogeneous porosity and lithology variations and the multiscale frac-ture networks present in carbonate reservoirs.25

Most carbonate reservoirs are naturally frac-tured—from microscale diffuse fractures (less than 1 m [3 ft]) to macroscale faults (greater than 100 m [330 ft]). At the intermediate meso-scale (10 to 100 m) subseismic faults and frac-ture swarms, or corridors, may prevail (above). A typical fracture corridor can consist of thousands of parallel fractures of variable dimensions densely packed together, forming a volume that is

typically a few meters wide, a few tens of meters high and several hundred meters long. Permeabilities in these corridors can range well above 10 darcies. These corridors often act as major conduits for fluid flowing within the reser-voir and may be responsible for early water breakthrough from natural drive or waterflood-ing. Therefore, to manage field production effec-tively and maximize total recovery, it is crucial that the locations of fracture corridors are accu-rately known and modeled.

24. For more on the computation of capillary pressure: Ouzzane J, Okuyiga M, Gomaa R, Ramamoorthy R, Rose D, Boyd A and Allen DF: “Application of NMR T2 Relaxation to Drainage Capillary Pressure in Vuggy Carbonate Reservoirs,” paper SPE 101897, presented at the SPE Annual Technical Conference and Exhibition, San Antonio, Texas, September 24–27, 2006.

25. Singh SK, Abu-Habbiel H, Khan B, Akbar M, Etchecopar A and Montaron B: “Mapping Fracture Corridors in Naturally Fractured Reservoirs: An Example from Middle East Carbonates,” First Break 26, no. 5 (May 2008): 109–113.

>Multiscale seismically constrained fracture characterization. Fractures may exist over a wide range of scales from very small cracks to very large faults. Understanding their distribution and properties at these different scales is essential to characterize naturally fractured reservoirs. The scales can be divided into three ranges: micro- (less than 1 m), meso- (10 to 100 m) and macro- (greater than 100 m). Microscale fractures include layer-bound diffuse fractures that can pervade across a geologic layer and are frequently observed in image logs such as those from the FMI fullbore formation microimager. Typically, these fracture types are the primary controls used to build geologic models containing fractures, such as implicit fracture models or discrete fracture networks (DFN). Although these diffuse fractures are smaller than surface seismic wavelengths, a large population density of such fractures can be detected with seismic measurements by analyzing the seismic anisotropy. Mesoscale fracture corridors and subseismic faults are the most difficult scale of fractures to characterize;

they are at the lower end of surface seismic resolution and few wells may intersect them. These narrow features cross layer boundaries and, with suitable 3D seismic data and careful analysis such as with the fracture cluster mapping workflow, they can be detected as subtle discontinuities in the data. Because mesoscale fracture corridors can have very high permeabilities and have major influence over reservoir dynamics, they should be incorporated into geologic models as individual fracture patch sets. In contrast to micro- and mesoscale fractures, macroscale faults are comparatively easy to detect with 3D seismic data and form the basis for structural modeling. Computer interpretation methods for fault detection, such as the ant tracking algorithm used in the Petrel seismic-to-simulation software, are available to automate the process and may be able to overcome analyst bias. Detailed analysis of the seismically derived rock properties around these faults may help in assessing fault transmissivity.

TS—Figure 22

Mac

rosc

ale

Faults Dislocated horizons Ant tracking, fault transmissivity Structural faults

Mes

osca

le

Fracture corridors Subtle discontinuities and scattering Fracture cluster mapping Fracture patch sets

Mic

rosc

ale

Geologic Features Seismic Observations Data Analysis Model Representations

Diffuse fractures Seismic anisotropy Anisotropy analysis and inversion Implicit fracture models or DFN

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54 Oilfield Review

One method for identifying these corridors using seismic data is the FCM fracture cluster mapping technique. Geoscientists have devel-oped the FCM workflow to identify discontinui-ties in the 3D surface seismic data associated with subseismic faults and fracture corridors. Two key factors contributing to the success of this technique are the suitability of the seismic acquisition and processing. The workflow assumes that large clusters of natural fractures, which constitute a fracture corridor, produce coherent structural discontinuities that are detectable with 3D seismic data. The complete FCM workflow integrates expert interpretation of high-quality seismic data and borehole mea-surements with geologic modeling and dynamic simulation, which enables a detailed character-ization of naturally fractured reservoirs.

The discontinuity extraction software identi-fies subtle inconsistencies that appear as linea-ments in the seismic data. Generally, the raw lineaments that are extracted are associated with either geologic discontinuities in the reser-voir or nongeologic residual features in the data such as acquisition footprints or near-surface noise contamination.26 To focus on detecting frac-ture clusters, the process is constrained and cali-brated with a priori knowledge that includes regional and local structural geology, tectonic history, reservoir geomechanics, core analysis, borehole images, sonic logs, vertical seismic pro-file data, well tests and production history.

Results are strongly dependent on the seismic acquisition geometry and data quality and will be less reliable with poor imaging, poor spatial and temporal bandwidth, low signal-to-noise ratio and acquisition footprints. Thus, there are strin-gent requirements on the 3D seismic data quality to provide a meaningful input for detecting frac-ture clusters. Custom design of processing and data acquisition, especially when using single-sensor data such as those provided by the Q-Land seismic system, may be necessary.27

The FCM technique offers a radically different technology for characterizing fractured reservoirs. Historically, only the properties of diffuse fractures have been characterized through the interpreta-tion of a variety of seismic attributes, such as azi-muthal anisotropy observations. However, with the fully integrated FCM workflow, the location of indi-vidual fracture corridors can be detected and embedded into a multiscale 3D reservoir model containing faults and diffuse fractures. Dynamic simulation of the fluid flow through these multi-scale models and calibration with production logs verify the major flow pathways. Operators can use this information to locate injector and producer wells to maximize reservoir sweep efficiency and minimize water breakthrough.

Locating the WellThe FCM workflow was used to model five Jurassic carbonate reservoirs in Kuwait. One of these fields, the Sabriyah field, was selected as the key area for study because of its challenging structural setting and a drilling schedule that

included four new wells (above left). An abun-dance of lineaments across the reservoir were identified after initial analysis of the seismic data. Further analysis of these lineaments revealed a predominant population oriented NNE-SSW along the main axis of the anticline structure and a secondary population consisting of orthogonal lineaments (next page). In con-trast, borehole image data showed a dominant ENE-WSW fracture orientation.

This analysis suggested that the dominant NNE-SSW trend in the lineaments is probably asso-ciated with longitudinal fold-related fractures and that the secondary set of orthogonal lineaments correlate with the fractures identified from the borehole image data and are possibly Riedel

26. Acquisition footprints, seen on 3D seismic time slices, are patterns that correlate to surface-acquisition geometry and distort amplitude and phase of reflections. This form of noise can obscure true subsurface reflections and should be removed prior to interpretation, if possible. Although the FCM workflow might detect them, an experienced interpreter should be able to identify them as noise rather than fractures.

27. The Q-Land system is a point-receiver acquisition and processing system capable of acquiring 30,000 channels of data in real time. Point-receiver data are recorded with variable densities and processed with

> Surface relief map of Sabriyah field in northern Kuwait. This field, the first of five to be analyzed, was considered a key area in the study. Geoscientists used the FCM workflow to evaluate existing seismic data. Wells X-5 and X-6 were to be drilled based on study results. Borehole images and core from these wells validated the fracture clusters predicted by the FCM model.

TS—Figure 19

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> Crosswell seismic imaging. At the absolute best, 3D surface seismic data (left) can resolve features down to tens of meters. Crosswell imaging, such as the DeepLook-CS seismic imaging service, acquires data from downhole sources and receivers placed in separate wells. Using higher frequencies extending to kilohertz provides ultrahigh-resolution images between wells and can resolve features as small as 1.5 m [5 ft]. Seen in the crosswell data (right) is a subseismic fault (magenta line) and the detailed multilayered reservoir structure. Fracture corridors, interpreted from discontinuities detected in a 3D seismic volume, can also be verified from this type of crosswell seismic imaging.

TS—Figure 23

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complementary digital group forming (DGF) techniques. DGF processed raw sensor measurements provide a clean “group-formed” trace with improved resolution and low noise.

28. Riedel shears produce a geometric fracture pattern commonly associated with strike-slip fault systems. They may form echelon patterns inclined 10° to 30° to the direction of motion.

29. Refae AT, Khalil S, Vincent B, Ball M, Francis M, Barkwith D and Leathard M: “Increasing Bandwidth for Reservoir Characterization with Single-Sensor Seismic Data,” Petroleum Africa (July 2008): 41–44.

30. The nominal fold is defined as the number of different source-receiver locations that illuminate a particular subsurface sampling point or bin. Each of the many source-receiver pairs, corresponding to a given bin location, will record reflections along different raypaths and can be characterized by its nominal azimuth and offset. A broad and uniform distribution of source-receiver offsets and azimuths within each bin provides more information for seismic reservoir characterization.

31. Singh et al, reference 25.

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shears.28 While this limited study indicated the presence of numerous structural discontinuities across the field that could be related to subseismic faults or fracture corridors, such interpretations can be validated only through further integration of other data sources and ultimately through drill-ing. An example of validation from other sources is the use of ultrahigh-resolution crosswell seismic imaging (previous page, top right).

To obtain more-detailed information about the fractures in the carbonate reservoirs of Kuwait, Kuwait Oil Company (KOC) acquired a state-of-the-art 3D seismic pilot survey over 100 km2 [38 mi2] of the Northwest Raudhatain field using the WesternGeco Q-Land technology. This system employs maximum displacement vibroseis sweep and single-sensor receivers (see “Land Seismic Techniques for High-Quality Data,” page 28). The MD Sweep technique enhances low-frequency content by optimally designing the drive force and variable sweep rate of the vibroseis units.29 Single-sensor deployment enables dense sampling of the wavefield for removal of source-generated noise.

The advanced acquisition design consisted of a wide-azimuth square patch, resulting in a very high nominal fold of 990 for 12.5-m by 12.5-m [41-ft by 41-ft] bin size with uniform offset- azimuth distribution up to 6 km [3.7 mi].30 This design is ideal for seismic fracture characteriza-tion using P-wave data. The Northwest Raudhatain field presents an additional challenge because the seismic reflections are contaminated by a series of multiple-reflected seismic waves that interfere with the primary reflections over the reservoir. Advanced data processing is currently being applied to suppress these multiples and maximize the extraction of information from the 3D seismic data for an extensive seismically guided fracture characterization.

In the past, engineers have proposed that fracture corridors result in early water break-through but did not have effective tools to detect their presence. Historically, fracture clusters detected in wellbores were incorporated in sto-chastic 3D models to explain their effects on pro-duction. The ability to identify fracture clusters

away from the wellbore using the FCM workflow and to visualize their orientation with 3D maps will help optimize field development and avoid unexpected water breakthrough.31

Hydrocarbons from CarbonatesMuch of the world’s remaining hydrocarbon reserves are thought to lie in carbonate rocks whose complexity has often confounded petro-leum engineers, geophysicists and geologists working to extract their riches. Step-change improvements in a wide variety of interpretation techniques and sensor technologies are making it possible for these professionals to more effectively evaluate, drill and produce carbonate reservoirs. By integrating techniques and technology, the sta-tistical odds inherent in drilling and maximizing recovery from carbonates are being shifted in favor of today’s petroleum technologists. —TS

> Refining and defining fracture clusters. Existing seismic data were processed using discontinuity extraction software (DES) models without filters (left), and the orientation of the fractures is overwhelmingly in line with the axis of the anticlinal structure (NNE-SSW). Logging data from Wells X-3 and X-4 indicated ENE-WSW orientation (insets). This is attributed to Riedel shears caused by NNE-SSW strike-slip faults. Azimuth filters applied to the seismic data detected fracture clusters with different orientations (right). The orientation of these clusters is masked in the original processing. (Adapted from Singh et al, reference 25.)

TS—Figure 20

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