resolving carbonate complexity- schlumberger

7/27/2019 Resolving Carbonate Complexity- SCHLUMBERGER

1/16

40 Oilfeld Review

Resolving Carbonate Complexity

Assessing basic rock properties using traditional logging suitesusually a straight-

orward process in sandstone reservoirsmay be difcult or impossible in carbonate

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

new wells rom petrophysical analysis oten 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 inormation, operators are able to drill and produce these reser-

voirs while better managing uncertainty.

Mariam Ibrahim Al-Marzouqi

Sultan Budebes

Emad Sultan

Abu Dhabi Marine Operating Company

Abu Dhabi, UAE

Iain Bush

Gatwick, England

Roger Grifths

Kais B.M. Gzara

Raghu RamamoorthyAbu Dhabi, UAE

Alexis Husser

Sugar Land, Texas, USA

Ziad Jeha

Juergen Roth

Ahmadi, Kuwait

Bernard Montaron

Beijing, China

Srinivasa Rao Narhari

Sunil Kumar SinghKuwait Oil Company

Ahmadi, Kuwait

Xavier Poirier-Coutansais

Mabruk Oil Company

Tripoli, Libya

Oilfeld ReviewSummer 2010: 22, no. 2.Copyright 2010 Schlumberger.

For help in preparation of this article, thanks to LisaStewart, 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.


2/16

Summer 2010 41

Characterizing and evaluating carbonate reser-

voirs rom conventional logging data can be

daunting. Traditional approaches that work per-

ectly well or determining basic petrophysical

properties in siliciclasticssuch as porosity,

saturation, permeability and rock mechanical

propertiesmay yield inaccurate results in car-

bonates. In addition to the diculties in evaluat-

ing rock properties, many carbonates have lateral

structural heterogeneities; rock properties varygreatly across the eld. Drilling to maximize pro-

duction can thus become a statistical exercise:

Drill enough wells and some will be successul.

Experts estimate that 60% o the worlds oil

reserves, as well as vast quantities o natural gas,

lie in carbonate reservoirs. The rewards or deci-

phering these enigmatic ormations are very

attractive. But to do so, petrophysicists and engi-

neers who evaluate and produce hydrocarbons

rom carbonates have learned that they must use

methods that dier substantially rom those used

or sandstones. Fortunately, new tools are avail-

able that increase analysts reservoir understand-

ing and decrease risks associated with eld

development and reservoir management.

This article describes several recently intro-

duced techniques, beginning at the drill bit and

extending to eldwide seismic studies that strive

to clariy carbonate complexity. Included are

advances in logging-while-drilling (LWD) technol-

ogy that help geologists overcome diculties they

encounter evaluating carbonates when using con-

ventional logging suites. We also review an inte-

grated sotware workfow that addresses

characteristics unique to carbonates. In addition,

a seismic workfow method is presented that, com-

bined with other data sources, identies high-

quality reservoir sections by detecting racture

corridors. Case studies rom the Middle East dem-

onstrate applications o these techniques.

The Problem with Carbonates

Carbonate sediments dier rom siliciclastics in

nearly every aspect: origin, deposition, diagene-

sis, oil lling and evolution.1 Because abundant

examples exist in the literature describing these

dierences, 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-

nicant opportunities or the development o

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 o the oil industry. They have developed

numerous classication systems that ocus on

particular carbonate peculiarities, such as tex-

ture, pore size and internal rock structure

(above).2 These eorts, however, do not equate to

understanding specic reservoir rock properties

in a given well or eld.

Diculties begin with quantiying basic insitu mineral, fuid and textural properties using

conventional logging tools. Petrophysicists use

these log data to characterize and identiy quality

reservoir rocks and guide drillers to the best pro-

ducing zones. Because o the complexities o car-

bonate reservoirs, evaluation programs oten rely

on conventional coring to decipher heterogene-

ities in rock properties. Coring provides lithology,

qualitative and quantitative estimation o porosity

1. For more on carbonates and carbonate evaluation:Akbar M, Petricola M, Wata M, Badri M, Charara M,Boyd A, Cassell B, Nurmi R, Delhomme J-P, Grace M,Kenyon B and Roestenburg J: Classic InterpretationProblems: Evaluating Carbonates, Oilfeld Review7,no. 1 (January 1995): 3857.

Akbar M, Vissapragada B, Alghamdi AH, Allen D, Herron MCarnegie A, Dutta D, Olesen J-R, Chourasiya RD, Logan D,

Stie D, Netherwood R, Russell SD and Saxena K:A Snapshot o Carbonate Reservoir Evaluation,Oilfeld Review12, no. 4 (Winter 2000/2001): 4260.

Ahr WM, Allen D, Boyd A, Bachman HN, Smithson T,Clerke EA, Gzara KBM, Hassall JK, Murty CRK, Zubari Hand Ramamoorthy R: Conronting the CarbonateConundrum, Oilfeld Review17, no. 1 (Spring 2005): 1829.

2. For more on carbonate classication systems:Scholle PA and Ulmer-Scholle DS: CarbonateClassication: Rocks and Sediments, in Scholle PA andUlmer-Scholle DS (eds): A Color Guide to the Petrographyo Carbonate Rocks: Grains, Textures, Porosity, DiagenesisTulsa: American Association o Petroleum Geologists,AAPG Memoir 77 (2003): 283292.

>Carbonate classication systems. The Dunham classication system (top), devised in 1964, is based onrock texture and grain size. (Adapted rom Akbar et al, 2000/2001, reerence 1.) The Ahr classicationsystem (bottom), published in 2005, maps pore geometry and attempts to relate stratigraphy to eld-leve

permeability predictions. (Adapted rom Ahr et al, reerence 1.) Although these parameters are importanor characterizing carbonate rock properties, neither classication system adequately describes keyreservoir storage capacity or fow characteristics.

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 sil t-size carbonate

Original components not bound together during deposition

Depositional texture recognizable

Depositional

Hybrid 1

Hybrid 2

Hybrid 3

FractureDiagenetic

Reduced

CompactionCementationReplacement

Diagenesis influencesbrittle behavior.

Depositional characterinfluences fractures.

Depositionalaspects dominate.

Enhanced

DissolutionReplacementRecrystallization

PorositySize and shape

Vugs separateVugs touching

Diageneticaspects

dominate.

InterparticleIntraparticleFenestralShelter or keystone

Reef

IntraskeletalInterskeletal

Stromatactis vugsConstructed voids

Detrital infill


3/16

42 Oileld Review

and permeability, and invaluable racture inor-

mation. Even when rock properties are quantied

or a particular well, measurement analogs

beyond the near wellbore may not be valid at res-

ervoir scales because o the inherent heterogene-

ity and diagenetic history o the carbonates within

the eld.

Petrophysicists must overcome a number o

diculties when evaluating carbonates. To begin

with, carbonates dier rom sandstones in that

they oten have some type o organic origin and are

more susceptible to chemical and mechanical

reactions. They usually consist o skeletons and

shells o animals that settled near where they

livedtypically in warm, shallow marine environ-

ments. Those biological structures were built rom

the calcium carbonate the animals extracted rom

seawater. The climatic conditions, the types o

organisms and the manner in which they existed

in their ecosystem all contribute to the reservoir

heterogeneity o carbonate structures.

By contrast, the particles that make up sand-

stone and mudstone deposits may travel thou-

sands o kilometers to reach their nal resting

place. Their size, shape and sorting have much to

do with the energy o the depositional environ-

ment. Because carbonate sediments usually are

not transported ar rom 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 o

biological origin have also been discovered.

These can cover wide expanses and also act as

hydrocarbon traps.

When the skeletal remains o biogenic car-

bonates stay where the organism lived, such as

coral or algal rees, geologists reer to these accu-

mulations as autochthonous.3 Lacking the inter-

granular permeability o clastics, these structures

usually require additional internal connectivity

to be productive, most oten in the orm o natu-

ral ractures (above). In contrast, allochthonous

carbonate deposits are composed o transported

shells and skeletal remains or bioclastic rag-

ments eroded rom reworked deposits.

Once the carbonate ragments come to rest,

they eventually become cemented together, gen-

erally with calcite, in a process o lithication.

Because these deposits can consist o ne-

grained particles or broken shell ragments, they

may have clastic characteristics similar to those

o sandstone. During lithication, the deposits

oten undergo chemical and biological diagene-

sis, which produces metastable compounds that

are susceptible to change (see Diagenesis and

Reservoir Quality, page 14). Ater 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 orm is cal-

cium carbonate, or calcite [CaCO3]. A less stablepolymorph, aragonite, has the same chemical

composition. Calcite is one o the more common

minerals on Earth, accounting or 4% by weight

o the Earths crust. Its chemical instability

makes it susceptible to transormation into

other mineral types.4 Siderite [FeCO3] can orm

when calcite is exposed to iron. Various other

carbonate varieties exist, each having character-

istic physical properties that aect matrix den-

sity and texture. The two most common

carbonate reservoir rocks are limestone and

dolomite. Limestone reers to the sedimentary

rock orm that contains calcite, although these

two terms are oten used interchangeably.

Determining the correct lithologybe it lime-

stone, dolomite or a combination o mineralsis

an important step in carbonate reservoir evalua-tion.5 Lithology establishes the matrix density, or

grain density, used or computing porosity rom

density tools. It is also an input or other porosity

measurements, such as those rom thermal and

epithermal neutron measurements. An accurate

porosity value is a crucial input or calculating

water and hydrocarbon saturations, determining

total fuid volumes and estimating reserves.

>Complexity o carbonates. The carbonate matrix oten tends to be complex and is composed ovarying concentrations o limestone, dolomite and other minerals. Vuggy acies may make up asignifcant portion o carbonate reserves. Wells with connectivity through vug-to-vug contact inracture networks generally are more prolifc producers than wells with matrix permeability alone.(Core slab photograph courtesy o the Whiting Petroleum Corporation, used with permission.)

>Matrix eects on density-porosity measurements.Density porosity is computed using a value ormatrix density. I the input is unknown or incor-rect, the density-porosity measurement error can

be substantial. For example, a 10% porositylimestone has a bulk density o 2.539 g/cm3. I therock is dolomite, the porosity is 17% with thatsame bulk density measurement. This 70% errorcould be the dierence between a commercialwell and abandonment.

Calculateddensityporosity,%

Limestone matrix

2.71 g/cm3

Dolomite matrix

2.85 g/cm3

20

15

10

5

0

density = density porosity

matrix = matrix density, or grain density

bulk = bulk density measurement

fluid = fluid density

matrix density

bulk

matrix

fluid

=

70% error


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Summer 2010 43

Measuring Basic Properties

Porosity is a basic petrophysical measurement,

usually obtained rom well logs. It is commonly

computed rom bulk density data. Density poros-

ity is sensitve to both the pore uids and the

matrix, especially the matrix. There are several

methods available or computing porosity, and

these oten are aected by the uids in the rock

and the mineralogy. Depending on environmen-

tal conditions and operational constraints, inte-grating these measurements plays a role in

decoupling the eects o the matrix on the

porosity value.

Examples o porosity measurements include

those rom 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 o gas, and NMR measurements are

also blind to porosity flled with tar, bitumen,

microporosity-bound water and hydrates.

In contrast to the NMR and neutron tools, bulk

density tools respond to both uid and lithology.

Density porosity (density) is computed using two

fxed inputs, matrix density (matrix) and uid den-

sity (uid), and the bulk density measured by the

tool (previous page, bottom). The uid density

used in calculating porosity is that o the uid fll-

ing the pores o the ormation, typically 1.0 g/cm3,

while the matrix density depends on the rock type.

The matrix density o 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 ormation has a measured bulk

density o 2.539 g/cm3. However, a dolomite matrix

could have the same measured bulk density but its

porosity would be 17%. I the rock type is not cor-

rectly identifed, this signifcant discrepancy in

the computed porositya 70% errormight be

the dierence between commercial viability and

the decision to abandon a well.

The matrix may be a single mineral type but is

oten a mixture. Small concentrations o minerals,

i unaccounted or, 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 o

2.98 g/cm3. Dispersed within the rock matrix, a

small percentage o anhydrite can signifcantly

increase the measured bulk density. When the

anhydrite is ound in the orm o nodules, the mea-

sured porosity will be lower than the true value

because logging tools average the response rom

both rock types (above). The ormation may

appear to be o poor quality, although the carbon-

ate portion may, in act, have good porosity and

permeability but be masked by the anhydrites

eects on the measurement.6

Low-porosity carbonates with heavy minerals,

such as anhydrite, are emerging as major sources o

bypassed hydrocarbons. Understanding the manner

in which these minerals aect porosity measure-

ments and reservoir producibility is crucial or

geologists who study carbonates. Core analysis

oten becomes a major actor in determining

commerciality o a feld. Logging data lack the

fne resolution o core analysis, but they provide a

continuous record o petrophysical propertie

such as porosity and lithology.

Complexity, Texture and Relative Permeability

Perhaps the most common lithology-determina

tion method rom logging data uses the photo

electric eect (PEF) measurement, which

responds primarily to the minerals in the orma

tion. This measurement is routinely acquired

using ormation density devices, such as the

Litho-Density and LWD density tools.7 Although

useul in dierentiating pairs o minerals among

sandstone, limestone, dolomite and anhydrite

additional measurements are required when

more than two minerals are present. Also, the

measurement is aected by barite in drilling-mud

systems, and borehole conditions such as thick

mudcake and hole rugosity may render it useless

A better method or solving complex litholo

gies and determining mineralogical concentrations, which may vary widely across a feld

depending upon the diagenetic history and uids

percolating through the reservoir, is an elementa

thermal neutron capture spectroscopy measure

ment. For example, the ECS elemental capture

spectroscopy and the LWD EcoScope tools oe

this type o measurement.8 These tools measure

the concentrations o specifc elements that cor

respond to mineralogy. Various matrix propertie

3. Vernon RH: A Practical Guide to Rock Microstructure.Cambridge, England: Cambridge University Press(2004): 3437.

4. There is disagreement on how dolomite orms in nature;

some scientists suggest that biogenic origins are theprimary 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, Oilfeld Review21,no. 3 (Autumn 2009): 3245.

5. For more on diculties with carbonate reservoirevaluation: Ramamoorthy R, Boyd B, Neville TJ,Seleznev N, Sun H, Flaum C and Ma J: A NewWorkfow or Petrophysical and Textural Evaluationo Carbonate Reservoirs, Petrophysics51, no. 1(February 2010): 1731.

>Mineralogical eects. Anhydrite is just one o many minerals ound withincarbonate reservoir rocks. The manner in which this mineral is dispersed mayaect fuid fow in the reservoir. It may also impact the porosity measurement.In the case o anhydrite nodules, the porosity o the reservoir rocks tends tobe underestimated and fuid fow is not greatly aected (core photograph,right). I the anhydrite is dispersed within the pore structure (micrograph, let),the porosity measurement will be reduced, as will fuid fow. (Adapted romRamamoorthy et al, reerence 5.)

Pore-filling anhydrite

Anhydrite nodule

6. Ramamoorthy et al, reerence 5.

7. The PEF is a log o photoelectric absorption (Pe) propertieso the rock matrix that is acquired along with ormationdensity measurements. Common minerals encountered in

oil and gas wells have specic Pe values: sandstone (1.9),dolomite (3.1), limestone (5.1) and anhydrite (5.0).

8. Japan Oil, Gas and Metals National Corporation (JOGMEC),ormerly Japan National Oil Corporation (JNOC), andSchlumberger collaborated on a research project todevelop LWD technology that reduces the need ortraditional chemical sources. Designed around thepulsed neutron generator (PNG), EcoScope service usestechnology that resulted rom this collaboration. ThePNG and the comprehensive suite o measurements ina single collar are key components o the EcoScopeservice that deliver game-changing LWD technology.


5/16

44 Oileld Review

can also be computed rom the yields, including

grain density.9 Grain density represents an eec-

tive matrix density and varies according to the

elements present in the ormation. It yields more-

accurate density porosity than when computed

using a xed-value matrix density.

Texture and pore geometry are also important

properties or identiying reservoir-quality rock

because knowledge o correct mineralogy and

porosity measurement alone is not sucient toiner fow characteristics in carbonate reservoirs.

In act, some experts believe that characteriza-

tion o pore geometry is the most important com-

ponent in carbonate evaluation.10 Complex pore

shapes and sizes oten result rom reservoir depo-

sition and the ensuing processes o dissolution,

precipitation and recrystallization. Although

time-consuming, core analysis can reliably iden-

tiy and quantiy pore geometry. The standard

resistivity and porosity measurements o a triple-

combo logging suite oten do not respond to

changes in pore size and texture. NMR data, how-

ever, have been shown to identiy changes in pore

size distribution not detectable by these conven-

tional logs (let).

To better evaluate reservoir rock quality using

logging data, experts developed a technique or

characterizing carbonate pore geometry by parti-

tioning the total porosity measurement into three

classes o pore spaces based on sizemicro-

(less than 0.5 microns), meso- (0.5 to 5 microns)

and macroporosity (larger than 5 microns). From

these partitions, reservoir quality and fuid-fow

properties are inerred.11 Partitioning o orma-

tion porosity by pore size uses specic ranges o

transverse relaxation times, T2, rom NMR data.12

Core data are oten used to rene T2 measure-

ment ranges (let).

Another partitioning method maps relative

pore geometry into eight rock classes (next page,

bottom let).13 The resulting ternary diagram was

rst developed through systematic analysis o

texture-sensitive borehole logs, which included

NMR data, borehole images, ull-waveorm acous-

tic logs and dielectric data.14 A similar ternary

diagram has been derived rom mercury injection

capillary pressure (MICP) tests on core.

For macroporosity evaluation, geophysicistshave recently begun to use acoustic data, such as

those rom the Sonic Scanner tool, to estimate

the raction o vuggy porosity. One application o

these data is to ne-tune the cementation expo-

nent, m, in Archies water saturation equation.

Vugs tend to increase the cementation exponent,

while large intergranular pores do not. Use o

macroporosity ractions rom NMR data alone

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

T2 Distributions

Depth,ft

X,050

X,000

0 100gAPI

Gamma Ray

6 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

PEF

1.95 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 numbero reasons, this practice was abandoned. However, the concept works airlywell or carbonates. Pore sizes are determined according to a range o T2distributions, and then the porosity is partitioned into macro-, meso- andmicroporosity based on these measurements. The longest T2 distributionscorrespond to macroporosity, large pores and vugs. The shortest T2distributions respond to microporosity. Oil migrating into water-flled rockdisplaces water in macro- and mesopores frst. Micropores generally remainwater flled.

Total porosity

Oil in place

0.5microns

5microns

Mesoporosity MacroporosityMicroporosity

Porositybelow shortT2 cutoff

NMRT2response

Porosityabove longT2 cutoff


6/16

Summer 2010 45

can result in elevated estimates om because the

measurement is based on pore size, not shape.

Combining vuggy porosity estimates rom ull-

waveorm acoustic data improves log-derived

estimations o the m exponent.

NMR data are also used to compute permea-

bility. The technique evolved rom 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 ractional ow in

hydrocarbon zones may, however, be derived

rom array resistivity log data when the well is

drilled with water-base mud.15 The invading mud

fltrate acts as an uncontrolled two-phase ow

experiment that can be analyzed in a manner

similar to relative permeability measurements

conducted on core.

This mud-fltrate invasion method not only

provides inormation about in situ ractional ow

and relative permeabilities, it also improves the

accuracy o ormation resistivity measurements

and water saturation estimates. The processing

involves orward modeling based on relative

permeability parameterization, radial invasion

models, petrophysical models and tool response

to specifc conditions. The inputs required or

computing water saturations using Archies equa-

tionormation water and bulk ormation resis-

tivitiesare more accurate when obtained using

this method, as are the ultimate computed uid

volumes. Even so, log analysts have discovered

that Archies equation may not be as reliable or

characterizing uids in carbonate reservoirs as itis in sandstones.

Whats Wrong with Archie?

In 1942 Gus Archie laid the oundation or mod-

ern log interpretation by introducing a relation-

ship linking water resistivity, ormation porosity

and ormation resistivity to uid saturation

(right). Variables in the equationa, m and n

are empirically ft based on reservoir characteris-

tics. In the absence o specifc data they are

generally assumed to equal 1, 2 and 2, respec-

tively.16 Assumptions in the ormulamorphol-

ogy o the pore space, connectivity o the pores

and wettability o the rockare best suited to

>A ternary diagram based on pore size. Carbonate pore geometry and size areinputs to this ternary diagram, which indicates reservoir quality. On the lower letside o the triangle, permeability is a unction o grain size. For the upper section,permeability is controlled by the volume o macropores. On the lower right, thepermeability is a unction o both grain and pore size.

k= 0.35 2 ( 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

= 1.0V

macro/(V

meso+V

micro) ][2

Carbonate rocks with abundantmacroporosity

Wepore throats)

ll-connected pores (large

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

100%microporosity

100%mesoporosity

Carbonate Pore System Classes and Permeability

100%macroporosity

2

>Archies water saturation equation (bottom).Porosity and Rtare log-derived measurements.Rwis either derived rom water salinity ormeasured rom produced water and converted todownhole temperature. Variables a, mand nareempirically t based on reservoir characteristics

They are assumed equal to 1, 2 and 2, respectivelyin the absence o specic data. A sensitivityanalysis (top) demonstrates the eects o varyingmand non computed water saturation. First, nis set to 2 and mis varied rom 2.3 to 1.7 (Track1). Next, mis xed and nis varied rom 2.5 to 1.0(Track 2). The baseline water saturation curveusing deault inputs or m= n= 2 is presented inboth tracks (red curve). (Adapted rom Griths eal, reerence 17.)

Sw= Archies water saturation

Rw= resistivity of formation water

Rt= 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

S

R

Ra

m

=

t

w

wn

9. For a thorough review o neutron capture spectroscopy:Barson D, Christensen R, Decoster E, Grau J, Herron M,Herron S, Guru UK, Jordn M, Maher TM, Rylander Eand White J: Spectroscopy: The Key to Rapid, ReliablePetrophysical Answers, Oilfeld Review17, no. 2(Summer 2005): 1433.

10. Archie GE: Classication o Carbonate Reservoir Rocksand Petrophysical Considerations, AAPG Bulletin36,no. 2 (1952): 278298.

11. Hassall JK, Ferraris P, Al-Raisi M, Hurley JF, Boyd A andAllen DF: Comparison o Permeability Predictors romNMR, Formation Image and Other Logs in a CarbonateReservoir, paper SPE 88683, presented at the Abu Dhabi

International Petroleum Conerence and Exhibition,Abu Dhabi, UAE, October 1013, 2004.

12. In NMR logging, transverse relaxation time, T2, resultsrom interactions o hydrogen atoms with theirsurroundings, including eects o bulk fuids, poresuraces and diusion in magnetic eld gradients.Short T2 times correspond to small pores, and longerT2 times correspond to larger pores.

13. Hassall et al, reerence 11.


15. For more on this technique: Ramakrishnan TS,Al-Khalia J, Al-Waheed HH and Cao Minh C:Producibility Estimation rom Array-Induction Logs

and Comparison with MeasurementsA Case Study,Transactions o the SPWLA 38th Annual LoggingSymposium, Houston, June 1518, 1997, paper X.

16. The a constant, a tortuosity or consolidation actor,was not in Archies original equation but was addedlater as a means o correcting or saturation in knownwater-lled reservoir rocks. For more on this subject:Archie GE: The Electrical Resistivity Log as an Aid inDetermining Some Reservoir Characteristics,Petroleum Transactions o AIME146 (1942): 5462.

Winsauer WO, Shearin HM, Masson PH and Williams MResistivity o Brine Saturated Sands in Relation to PoreGeometry, AAPG Bulletin36, no. 2 (1952): 253277.


7/16

46 Oileld Review

siliciclastic rocks.17 Although most water satura-

tion methods utilize some orm o Archies 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 o his equation in car-

bonate evaluation.18

In addition, the complex nature o carbonates

makes determination o the a, m and n variables

dicult, and these values may change rapidly

throughout the reservoir.19 Other problems with

using Archies saturation equation in carbonates

include matrix complexity, pore size heterogene-

ity, pore shape and distribution, variability in or-

mation water salinity and uncertainty in the true

ormation resistivity measurement.

The process o lling the reservoir creates

some o the diculties encountered when using

Archies water saturation equation: Water lls

the pores initially and then hydrocarbons enter,

charging the complex carbonate structure. The

macropores ll rst, because they have the low-

est capillary entry pressure. A proportion o the

mesopores ll next and, because o capillary

pressure, micropores may remain water lled. As

a result o the basic nature o carbonate grain

suraces, there is an anity or crude oil, which

typically contains acidic components. Hence, the

pores that ll with oil may become oil wet, while

micropores that never ll with oil remain water

wet. This results in a mixed-wettability rock.

Moved by natural or injected water sweeping

through producing elds or by ltrate during

drilling, reservoir fuids are displaced in the larg-

est pores rst. Because o the altered wettability

in the rock, these pores present the least resis-

tance to the ingress o the fuids. Fluid capillary

eects and dierences between the original

charging pressure and reservoir pressure during

production may result in some o the mesopores

remaining oil lled even as the macro- and micro-

pores are water lled. This creates a complex

fuid distribution inside the pore network. Thus,

Archie parameters are dierent or the invaded

rock o the near-wellbore area than or the unin-

vaded zones o the same rock (above).

The complex wettability o carbonates makes

use o Archies saturation equation problematic

as well. Unlike sandstone reservoirs that are usu-

ally strongly water wet, most carbonate reservoir

rocks have some degree o moderate oil-wet char-

acter. Preerentially oil-wet suraces, located on

the walls o meso- and macropores, have been in

contact with oil. This reduces the connectivity o

the water phase in the porous rock and contrib-

utes to an increase in the resistivity compared

with the value predicted by Archies equation.

On the other hand, micritic grainstightly

packed micron-size calcite crystals with sub-

micron poresare ully water saturated and

water wet and dramatically enhance the connec-

tivity o water in the medium. The eect o

micrite counteracts the eect o oil-wetness on

the rocks electrical properties. Carbonate rocks

with a large volume raction o micrite may have

a resistivity similar to that o shaly sandstone

rocks. Carbonate rocks with little or no micriticcontent, such as dolomite, may have a pro-

nounced opposite response typical o oil-wet

rocks. These resistivity behaviors can be modeled

by the connectivity equation.20

In Archies saturation equation, the term or

ormation water, Rw, assumes a simple fuid distri-

bution with a single value o ormation water resis-

tivity. Complex fuid distributions, such as mixed

ltrate or injection waters, are a departure rom

>Carbonate reservoir lling and resistivity measurements. Water (blue) originally lls the pore spaces o carbonatereservoirs (left). As oil (green) migrates into the rock, large pores ll rst. I there is no connectivity, some pores may remainwater lled (center). Because resistivity tools measure through a path o least resistance (red line), the current may bypassoil-lled pores (right), which will increase the measured resistivity. Thus the resistivity values may be substantially lowerthan expected and not be representative o the true bulk resistivity.

Micropores

MesoporesMacropores Water-filled vug Path of least resistance

>Sigma equation or water saturation. Standard values or the matrix sigma, grain, are shown (top),although the measurement can be rened with spectroscopy data. Values or water can be calculatedusing fuid salinity, computed rom log responses or directly measured rom produced water samples.This equation (bottom) provides a water saturation value that is not based on resistivity measurements.

Lithology

Sandstone = 4.3Dolomite = 4.7

Calcite = 7.1

Anhydrite = 12

, cu 0 5 10 15 20 25 30 35

Clays

40 45 50

Fluid Gas Oil Fresh water Increasing salinity

Sw= formation water saturation

= formation porosity

bulk = measured formation capture cross section

water = capture cross section of the water

grain = formation grain capture cross section

HC = hydrocarbon capture cross section

bulk grainw =

( ) grain HC( )

water HC( )

S


8/16

Summer 2010 47

the model. The true resistivity in the reservoir, Rt,

can be dicult to measure as well. Water-lled

microporosity and mesoporosity provide paths o

lower resistance or the sensor current. Thus the

average bulk resistivity measured in the ormation

is signicantly lower than that in rocks with identi-

cal porosity and fuid saturations but with uni-

modal pore-throat or pore-body size distributions.

These occurrences, reerred to as low-resistivity

pay ormations, have led to underestimated hydro-carbon reserves and bypassed hydrocarbons.

For these and other reasons, Archies satura-

tion equation is unlikely to be accurate or car-

bonates without making empirical adjustments

to the input variables. An alternative to Archies

equation derives saturation rom the macro-

scopic thermal neutron capture cross section

measurement, or sigma (, measured in capture

units, cu), which has been used or cased hole

evaluation or many years. A pulsed-neutron gen-

erator (PNG) emits high-energy neutrons that

interact with the nuclei o the elements present

in the surrounding ormation. O the elements

generally ound in the reservoir, chloride ions

[Cl], primarily ound in salt water, have the

greatest neutron capture capacity, also reerred

to as capture cross section. The rate o neutron

capture is predominately a unction o chloride

concentration, which can be related to the vol-

ume and salinity o the ormation water.

Hydrocarbons have a low capture capacity, and as

long as there is sucient salinity in the orma-

tion water to produce a usable sigma contrast

between hydrocarbons and water, sigma can be

used to compute water saturation.

Inputs or computing water saturation using

sigma are porosity and macroscopic capture cross

section or ormation matrix (grain), ormation

water (water), expected hydrocarbons in place

(HC) and the sigma measured by the tool (bulk)

(previous page, bottom). I the lithology is known,

matrix sigma can be input as a constant, or it can

be derived rom the elemental thermal neutron

capture spectroscopy measurement in a manner

similar to determining grain density or porosity

calculations. The value owater can be measured

directly, estimated rom downhole measurements

or calculated rom the salinity o produced sam-ples. Finally, HC, a constant used in the satura-

tion equation or the hydrocarbon type, is derived

rom expected fuid properties at downhole tem-

perature and pressure.

The depth o investigation o the sigma

measurement is quite shallow compared with

that o resistivity measurements. Thus the ability

to characterize the uninvaded portion o the res-

ervoir may be signicantly hindered because

mud ltrate invades the near-wellbore zone dur-

ing the drilling process. The sigma measurement

may respond primarily to the ltrate. As a conse-

quence, wireline sigma measurements acquired

in open hole have not proved useul or 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 ormations at irreducible

water saturation with oil-base mud. In this case

the time o the measurement does not matter, but

the assertion that the ormation is at irreducible

water saturation must be validated.

Cased hole sigma logs have proved more

benecial than openhole logs because they are

acquired ater the ltrate has dissipated. Evenso, the measurement may be degraded by the

eects o casing, cement and residual fuids. This

has led to dierences between saturations mea-

sured with cased hole tools and those derived

rom openhole logs.

An alternative to openhole and cased hole

sigma measurements rom wireline tools is sigma

measured using an LWD tool. Depending on such

actors as drilling rate o penetration (ROP), or

mation porosity, ormation permeability, mud

properties, mud pressure overbalance and the

elapsed time between the rst drilling in the or

mation and the time o acquiring the sigma mea

surement, the invaded zone may not extend into

the region o the measurements depth o investi

gation. Acquiring data close behind the drill bit

and prior to invasion overcomes many o the limi

tations o sigma acquisition using wireline meth

ods. This capability has been available or severa

years with the EcoScope tool, a multiunction

LWD service that combines resistivity sensors

with a PNG or sigma and sourceless thermal neu

tron porosity logging (above). The EcoScope too

17. Grifths R, Carnegie A, Gyllensten A, Ribeiro MT,Prasodjo A and Sallam Y: Evaluation o Low ResistivityPay in CarbonatesA Breakthrough, Transactions ofthe SPWLA 47th Annual Logging Symposium, Veracruz,Mexico, June 47, 2006, paper E.

18. Grifths et al, reerence 17.

19. Grifths et al, reerence 17.

20. For more on wettability and carbonates, especiallymodeling o resistivity: Montaron B: ConnectivityTheoryA New Approach to Modeling Non-ArchieRocks, Transactions of t he SPWLA 49th Annual LogginSymposium, Edinburgh, Scotland, May 2528, 2008,paper GGGG.

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

Phase resistivity

Attenuation resistivity

Sigma, sourceless neutronporosity, spectroscopy andneutron-gamma density

Azimuthal densityand PEF


9/16

48 Oileld Review

is considered sourceless because once power

which is generated rom mud fowing through the

toolis no longer applied to the PNG, it ceases to

emit neutrons. Conversely, chemical sources are

always on.

The neutron output rom the PNG also makes

thermal neutron capture spectroscopy measure-

ments possible. Similar to the measurements

rom the wireline ECS tool, the EcoScope spec-

trometry service delivers elemental yields o sili-con [Si], calcium [Ca], iron [Fe], sulur [S],

titanium [Ti], gadolinium [Gd], potassium [K],

hydrogen [H] and chlorine [Cl]. Although the

EcoScope tool was not able to dierentiate lime-

stone rom dolomite in the past, the tool response

was recently recharacterized to include a magne-

sium [Mg] measurement (below). The ability to

measure Mg is undamental or distinguishing

dolomite rom limestone. In barite-weighted mud

systems, this becomes a crucial measurement or

determining ormation lithology because the PEF

measurement rom a Litho-Density tool is ren-

dered unusable by the eects o the barite. In

complex mineralogy the spectroscopy measure-

ment helps identiy mineral constituents and pro-

vides an eective matrix density, or grain density,

or more-accurate density-porosity computations.

Complex Middle East Carbonate

Recently the EcoScope tool was run in an oshore

Abu Dhabi carbonate eld.21

Production rom thiseld began in 1968 rom Lower Cretaceous, Upper

Jurassic, Upper Permian and Lower Triassic or-

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 t] thick

and comprises two intervals separated by shale.

The lower interval is a micro- to very ne-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 rom 6% to

8%, and permeability ranges rom very low to low.

The Gulailah reservoir is 250 m [820 t] thick,

with alternating dolomitic and anhydritic beds.

The dolomites are sucrosic to nely crystalline,

anhydritic and occasionally argillaceous. Porosity

ranges rom 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 signicantly degraded the PEF mea-

surement. The EcoScope tools spectroscopy

measurement was able to accurately distinguish

calcite rom dolomite and provide the matrix

grain density.

Another common complication encountered in

evaluating deviated wellsespecially in carbon-

atesis resistivity anomalies caused by shoulder-

bed eects. These arise when the measurement

volume includes regions with large conductivity

contrasts. Electromagnetic averaging and charge

buildup along the interace between layers result

in polarization horns, seen as anomalous spikes in

the resistivity data(next page).22

Although shoulder-bed eects are generally

small in vertical wells, or deviated and horizon-tal wells these eects may be prominent in long

intervals as wells approach, intersect and depart

rom layer boundaries. Resistivities aected by

shoulder beds can produce misleadingly high

hydrocarbon saturations when calculated using

Archies saturation equation.

>Rening lithology determination. Standard SpectroLith processing (left)cannot distinguish calcite rom dolomite in the absence o a PEF ormagnesium measurement and assumes that all calcium is associated withcalcite. When lithology is computed using the PEF measurement rom aLitho-Density tool, the sotware is able to distinguish dolomite rom calcite

(center), but the PEF measurement can be aected by barite in the drillingfuids and by hole conditions. The excessive anhydrite shown in the centertrack is attributed to these eects. I more than two minerals are present,the PEF measurement is less accurate. Spectroscopy that includes amagnesium measurement (right) distinguishes dolomite rom calcite and isnot aected by hole conditions and fuid properties. Other minerals can beaccurately quantied 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


10/16

Summer 2010 49

The superiority o sigma-based saturation

measurements over conventional methods is

compromised in the presence o signifcant mud-

fltrate invasion. Resistivity-response modeling

has shown that invasion less than 5 cm [2 in.] has

negligible eects on the sigma measurement.

Generally, because the measurement is taken so

close to the bit, the ormation does not have time

to become signifcantly invaded beore the

EcoScope tool acquires data. The tools resistivity

sensor array, collocated with the sigma measure-

ment, can determine the degree o invasion in

the area sampled.

21. Grifths R and Poirier-Coutansais X: ComplexCarbonate Reservoir EvaluationA Logging WhileDrilling Field Example, paper AA, presented at theSPWLA Regional Symposium, Abu Dhabi, UAE,April 1618, 2007.

22. Grifths and Poirier-Coutansais, reerence 21.

>Shoulder-bed eects on LWD resistivity measurements. Averaging o resistivity measurements aects the output atbed boundaries. In wells drilled nearly perpendicular to the layering (top left), these eects tend to be localized asthe tool crosses a resistivity interace. Horizontal wells may cross multiple zones with large resistivity contrasts (topright). In this situation, charges accumulate at the interace and induce a polarization horn, or spikeswhich aredependent on the depth o investigationthat are not representative o the actual resistivity ( middle). I notaccounted or during interpretation, the elevated resistivities produce misleadingly high hydrocarbon saturationsusing Archies saturation equation. The sigma measurement (bottom) does not suer rom the polarization eect,permitting a more accurate evaluation o the hydrocarbon saturation in high-angle wells.

1 ohm.m

50 ohm.m

Resistivity,

ohm.m

5,000 5,010 5,020

Distance from boundary, ft

5,030 5,040

1,000

100

10

1

1 ohm.m 50 ohm.m

S

igma,

cu

5,000 5,010 5,020

Distance from boundary, ft

5,030 5,040

1,000

100

10

1

1 ohm.m 50 ohm.m

+ +

1 ohm.m

50 ohm.m


11/16

50 Oileld Review

In the Total well, the preinvasion sigma rom

the EcoScope tool provided a valid water satura-

tion measurement independent o ormation

resistivity. As an added benet, petrophysicists

were able to determine appropriate inputs to

Archies water saturation equation to match the

sigma-based measurement. Because carbonate

reservoirs oten have unknown Rw values, simul-taneously solving or water salinity provided a

realistic Rw and water output that satised both

equations (above).

Sum Greater than Parts

The EcoScope approach provides answers about

fuid saturations in carbonates, but a preinvasion

sigma measurement is oten unavailable.

Recognizing the challenges in carbonate

evaluation, Schlumberger scientists devised a

workfow or petrophysical and textural evalua-

tion that integrates standard wireline logging

suites with recently introduced measurements.

Several independent research eorts ocusing on

discrete aspects o carbonate evaluation are com-

bined using this systematic methodology. The

workfow evolved into the Carbonate Advisor sot-ware program (next page, top let). Each step in

the workfow provides a piece o the puzzle and

acilitates subsequent steps.

Petrophysicists applied this methodology to a

Cretaceous Middle East carbonate well that had

a comprehensive suite o wireline logs. The log-

ging program included array resistivity (both

induction and laterolog), gamma ray, density,

thermal and epithermal neutron, NMR, ull-wave-

orm acoustic, neutron capture spectroscopy and

microresistivity imaging tools.

The analysis hierarchy began with lithology

and mineralogy determinations rom fuid- and

matrix-sensitive data, including NMR inorma-

tion, density and neutron porosity logs, PEF logs

and neutron capture spectroscopy data. The pet-

rophysicist can emphasize the importance o aparticular measurement based on its relevance

and the borehole environment to obtain a simul-

taneous solution that includes input rom all

measurements.23 In this case the mineralogy con-

sists predominantly o calcite with small amounts

o dolomite. Siliciclastic material and anhydrite

were also observed (next page, top right).

Elemental thermal neutron capture spectros-

copy data quantied the dolomite, anhydrite,

> Improved Archies equation and sigma saturation measurements. Apparent ormation salinity is computed assuming theormation is 100% water saturated (Tracks 3 and 5, green curves). Apparent salinity rom the spectroscopy chlorine/hydrogen(Cl/H) ratio measurement (Tracks 3 and 5, blue curve) is presented or comparison. Archie saturation is calculated using nand mexponents set to 2 and an Rw based on the assumed salinity corrected or downhole conditions (Tracks 4 and 6, blue curve).Sigma-based saturations (red curve) are computed using two dierent water salinities: 250 and 150 parts per thousand (ppt).The red lines in Tracks 3 and 5 indicate the salinity input used or 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, theSpectroLith 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 saturationmeasurement and a more reasonable choice or ormation-uid salinity. Note the lack o separation between deep and shallowresistivities (Track 1) indicating shallow invasion and acceptable sigma measurement. Neutron and density porosities, adjustedor matrix lithology rom spectroscopy data, are also presented (Track 2). (Adapted rom Grifths and Poirier-Coutansais,reerence 21.)

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 Water

Irreducible Water

Clay-Bound Water

Free Water

Irreducible Water

40-in. Blended LWD Tool

40-in. 2-MHz Phase Shift



400 ppt 4

Clay-Bound Water


12/16

Summer 2010 51

quartz and clay (illite) volumes to generate an

eective 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 fne-tuned with borehole image and ull-waveorm acoustic data. In

contrast to the lithology and mineralogy, the pore

geometry was highly variable, with zones contain

ing signifcant amounts o macroporosity inter

spersed with zones dominated by mesoporosity

and lesser amounts o microporosity(let).

> Integrated carbonate solution. This owchart shows the workow sequenceor analyzing carbonate reservoirs using Carbonate Advisor sotware.

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

Integrated

carbonate

evaluation

Capillary pressures

Fluid saturations

Fractional flow

>Lithology defned by the ECS tool. Themeasurement principle or neutron capturespectroscopy is the same or both the ECS andthe EcoScope tools; the dierence is the neutronsource. The ECS sonde has a chemical sourceand the EcoScope tool uses a pulsed-neutrongenerator with a higher neutron output.Traditional methods or determining lithology usePEF data rom a Litho-Density tool (left). Thismethod is best suited or two-mineral models. Byadding elemental yield data rom the ECS tool(right), the lithology can be refned, providing amore accurate density-porosity measurement

because the grain density reects the truemineralogy. The porosity dierence betweenusing a fxed limestone matrix density value andan eective grain density computed rom ECSmineralogy is presented (Track 2, orangeshading). (Adapted with permission o theSPWLA rom Ramamoorthy et al, reerence 5.)

Anhydrite

Calcite

Dolomite

Illite

Dolomite

Calcite

Anhydrite

Quartz

Bound Water

Porosity Correction


>Porosity partitioning o NMR data. The distribution o T2 transverse relaxation time data (Track 1) romthe NMR tool is partitioned based on cutos that can be refned rom core analysis. In this examplevolumes computed rom distributions to the let o the red line (Track 1) represent microporosity, whichcorrespond to the blue shaded volume in Track 2. Microporosity measurements rom core are plottedalong with the microporosity volume or confrmation. The area between the red and blue lines in Track 1is mesoporosity, corresponding to the green shading in Track 2. The macroporosity (red shading) isassociated with remaining porosity (Track 1, right o the blue line). Permeability rom core data isplotted with permeability computed rom NMR data (Track 3). The ree-uid volume computed romNMR data can be similarly partitioned (Track 4). Fluid volume to the right o the cuto (blue line) isassociated with mesoporosity, and the volume to the let is macroporosity. Core data points agree withcomputed data. (Adapted rom Ramamoorthy et al, reerence 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 Macroporosity

30 % 0

Macroporosity Cutoff

30 % 0

Free Fluid, NMR

Microporosity

Mesoporosity

MacroporosityT2 Distributions

T2 Cutoff Short

T2 Cutoff Long


13/16

52 Oileld Review

The partitioned porosity rom NMR data had

good correlation with data rom 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 fuid saturations

were computed using both array induction and

array laterolog resistivity measurements. Because

o the high salinity o the borehole fuid, the induc-

tion measurement was unreliable at high resistivi-

ties in the main hydrocarbon section. The laterolog

data are preerred in these zones.

Drainage capillary pressures were also com-puted based on NMR data transorms.24 Because

the NMR data provide pore size rom T2 distribu-

tions, assuming bulk and diusion eects 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 rom simi-

lar core samples. Using the Carbonate Advisor

program, the analyst manually determines the

constant by comparing MICP entry pressures

with those computed rom NMR log data.The integrated approach o the Carbonate

Advisor sotware provides comprehensive evalua-

tion o key properties that describe reservoir

storage capacity and fow characteristics(above).

The sotware ollows a set workfow, but through-

out the process the petrophysicist has interactive

control over how data are input, a particularly

useul eature when measurement conditions

may be less than optimal.

> Integrated output. Shown is the nal product rom the Carbonate Advisorprogram. These outputs provide an integrated and comprehensiveevaluation o the key properties that describe a reservoirs storage and fowcapacity. The petrophysicist may weight the data rom specic tools andchoose between tools (Depth track, AIT array induction imager tool, green;and HRLA high-resolution laterolog array, gold). Complex lithology and fuidvolumes (Track 1) are shown along with a moved-hydrocarbon analysis(orange) rom microresistivity data. Fluid-fow models are constructed romresistivity data (Track 2). Porosity rom NMR data (Track 3) are partitionedand the results graphically displayed (Track 4). A ull ternary analysis (Track 5)

is useul or identiying better quality reservoir rock. Drainage capillarypressures are computed rom NMR pore geometry data, adjusted to matchMICP data when available, and then plotted with water saturation (Track 6).The dark-blue shading indicates the pore space that can become oil lled atlow capillary pressure. The shading transitions rom blue to red,corresponding to successively higher capillary pressures required to lladditional pore volumes. Thus the layer around X,600, with more dark-blueshading than the mostly red and yellow layer around X,500, representsbetter quality rock. (Adapted rom Ramamoorthy et al, reerence 5.)

AIT Tool

Moved Hydrocarbon

Hydrocarbon

Water

Depth,ft

Pyrite

Quartz

Anhydrite

Calcite

Dolomite

HRLA Tool

Siderite

Kaolinite

Chlorite

Illite (dry)

Montmorillonite

Lithology

Contributing Flow

0 % 100

T2 Distributions

50 0%

Core Porosity

Total Porosity

50 0%

Microporosity

Macroporosity

Mesoporosity

NMR Porosity Partition

Computed Permeability

0.1 10,000mD

Core Permeability

0.1 10,000mD

Microporosity

Micromesoporosity

Micromacroporosity

Mesomicroporosity

Macromicroporosity

Mesoporosity

Macromesoporosity

Macroporosity

Ternary Porosity Partition

X,400

X,500

X,600

Capillary PressureMin Max

100%0

Water Saturation


14/16

Summer 2010 53

Searching Above Ground

Approaches discussed so ar apply to data acquired

downhole. Because o the heterogeneity o carbon-

ate reservoirs, the shallow depth o investigation

o most logging tools may limit their use or opti-

mizing well positioning. For instance, racture ori-

entation obtained rom imaging tools can be

inuenced by local eects and may not reect the

predominant trend in the reservoir. However, new

developments in seismic technology are providing

operators with assistance in detecting ractureswarms within a reservoir and this knowledge can

be used to optimize well locations.

Three-dimensional surace seismic surveys

oer an expanded view o reservoir heterogene-

ity, extending over the entire feld. 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 actors such as near-suracegener-

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 fdelity are now obtainable.

Consequently, geoscientists are able to charac-

terize in fner detail the heterogeneous porosity

and lithology variations and the multiscale rac-

ture networks present in carbonate reservoirs.25

Most carbonate reservoirs are naturally rac-

turedrom microscale diuse ractures (less

than 1 m [3 t]) to macroscale aults (greater

than 100 m [330 t]). At the intermediate meso-

scale (10 to 100 m) subseismic aults and rac-

ture swarms, or corridors, may prevail (above). A

typical racture corridor can consist o thousands

o parallel ractures o variable dimensions

densely packed together, orming a volume that is

typically a ew meters wide, a ew tens o meter

high and several hundred meters long

Permeabilities in these corridors can range wel

above 10 darcies. These corridors oten act a

major conduits or uid owing within the reser

voir and may be responsible or early wate

breakthrough rom natural drive or waterood

ing. Thereore, to manage feld production eec

tively and maximize total recovery, it is crucia

that the locations o racture corridors are accu

rately known and modeled.

24. For more on the computation o capillary pressure:Ouzzane J, Okuyiga M, Gomaa R, Ramamoorthy R,Rose D, Boyd A and Allen DF: Application o NMR T2Relaxation to Drainage Capillary Pressure in VuggyCarbonate Reservoirs, paper SPE 101897, presented atthe SPE Annual Technical Conerence and Exhibition,San Antonio, Texas, September 2427, 2006.

25. Singh SK, Abu-Habbiel H, Khan B, Akbar M, Etchecopar Aand Montaron B: Mapping Fracture Corridors inNaturally Fractured Reservoirs: An Example romMiddle East Carbonates, First Break26, no. 5(May 2008): 109113.

>Multiscale seismically constrained racture characterization. Fracturesmay exist over a wide range o scales rom very small cracks to very largeaults. Understanding their distribution and properties at these dierentscales is essential to characterize naturally ractured reservoirs. The scalescan be divided into three ranges: micro- (less than 1 m), meso- (10 to 100 m)and macro- (greater than 100 m). Microscale ractures include layer-bounddiuse ractures that can pervade across a geologic layer and arerequently observed in image logs such as those rom the FMI ullboreormation microimager. Typically, these racture types are the primarycontrols used to build geologic models containing ractures, such as implicitracture models or discrete racture networks (DFN). Although these diuseractures are smaller than surace seismic wavelengths, a large populationdensity o such ractures can be detected with seismic measurements by

analyzing the seismic anisotropy. Mesoscale racture corridors andsubseismic aults are the most dicult scale o ractures to characterize;

they are at the lower end o surace seismic resolution and ew wells mayintersect them. These narrow eatures cross layer boundaries and, withsuitable 3D seismic data and careul analysis such as with the racturecluster mapping workfow, they can be detected as subtle discontinuities inthe data. Because mesoscale racture corridors can have very highpermeabilities and have major infuence over reservoir dynamics, theyshould be incorporated into geologic models as individual racture patchsets. In contrast to micro- and mesoscale ractures, macroscale aults arecomparatively easy to detect with 3D seismic data and orm the basis orstructural modeling. Computer interpretation methods or ault detection,such as the ant tracking algorithm used in the Petrel seismic-to-simulationsotware, are available to automate the process and may be able toovercome analyst bias. Detailed analysis o the seismically derived rock

properties around these aults may help in assessing ault transmissivity.

Macroscale

Faults Dislocated horizons Ant tracking, fault transmissivity Structural faults

Mesoscale

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

Microscale

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 Oileld Review

One method or identiying these corridors

using seismic data is the FCM racture cluster

mapping technique. Geoscientists have devel-

oped the FCM workfow to identiy discontinui-

ties in the 3D surace seismic data associated

with subseismic aults and racture corridors.

Two key actors contributing to the success o

this technique are the suitability o the seismic

acquisition and processing. The workfow

assumes that large clusters o natural ractures,

which constitute a racture corridor, produce

coherent structural discontinuities that are

detectable with 3D seismic data. The complete

FCM workfow integrates expert interpretation

o high-quality seismic data and borehole mea-

surements with geologic modeling and dynamic

simulation, which enables a detailed character-

ization o naturally ractured reservoirs.

The discontinuity extraction sotware identi-

es 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 eatures in the data

such as acquisition ootprints or near-surace

noise contamination.26 To ocus on detecting rac-

ture clusters, the process is constrained and cali-

brated with a priori knowledge that includesregional and local structural geology, tectonic

history, reservoir geomechanics, core analysis,

borehole images, sonic logs, vertical seismic pro-

le 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 ootprints. Thus, there are strin-

gent requirements on the 3D seismic data quality

to provide a meaningul input or detecting rac-

ture clusters. Custom design o 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 oers a radically dierent

technology or characterizing ractured reservoirs.

Historically, only the properties o diuse ractures

have been characterized through the interpreta-

tion o a variety o seismic attributes, such as azi-

muthal anisotropy observations. However, with the

ully integrated FCM workfow, the location o indi-

vidual racture corridors can be detected and

embedded into a multiscale 3D reservoir model

containing aults and diuse ractures. Dynamic

simulation o the fuid fow through these multi-

scale models and calibration with production logs

veriy the major fow pathways. Operators can use

this inormation to locate injector and producer

wells to maximize reservoir sweep eciency and

minimize water breakthrough.

Locating the Well

The FCM workfow was used to model ve

Jurassic carbonate reservoirs in Kuwait. One o

these elds, the Sabriyah eld, was selected as

the key area or study because o its challenging

structural setting and a drilling schedule that

included our new wells (above let). An abun-

dance o lineaments across the reservoir were

identied ater initial analysis o the seismic

data. Further analysis o these lineaments

revealed a predominant population oriented

NNE-SSW along the main axis o the anticline

structure and a secondary population consisting

o orthogonal lineaments (next page). In con-

trast, borehole image data showed a dominant

ENE-WSW racture orientation.

This analysis suggested that the dominant

NNE-SSW trend in the lineaments is probably asso-

ciated with longitudinal old-related ractures and

that the secondary set o orthogonal lineaments

correlate with the ractures identied rom the

borehole image data and are possibly Riedel

26. Acquisition ootprints, seen on 3D seismic time slices,are patterns that correlate to surace-acquisitiongeometry and distort amplitude and phase o reections.This orm o noise can obscure true subsuracereections and should be removed prior tointerpretation, i possible. Although the FCM workowmight detect them, an experienced interpreter shouldbe able to identiy them as noise rather than ractures.

27. The Q-Land system is a point-receiver acquisition andprocessing system capable o acquiring 30,000 channelso data in real time. Point-receiver data are recordedwith variable densities and processed with

>Surace relie map o Sabriyah feld in northernKuwait. This feld, the frst o fve to be analyzed,was considered a key area in the study.Geoscientists used the FCM workow to evaluateexisting seismic data. Wells X-5 and X-6 were tobe drilled based on study results. Boreholeimages and core rom these wells validated theracture clusters predicted by the FCM model.

X-6

X-5

X-1

X-4

X-3

X-2

2 km

1 mi

>Crosswell seismic imaging. At the absolute best,3D surace seismic data (left) can resolve eaturesdown to tens o meters. Crosswell imaging, suchas the DeepLook-CS seismic imaging service,

acquires data rom downhole sources andreceivers placed in separate wells. Using higherrequencies extending to kilohertz providesultrahigh-resolution images between wells andcan resolve eatures as small as 1.5 m [5 t]. Seenin the crosswell data (right) is a subseismic ault(magenta line) and the detailed multilayeredreservoir structure. Fracture corridors, interpretedrom discontinuities detected in a 3D seismicvolume, can also be verifed rom this type ocrosswell seismic imaging.

X,950

Depth,ft

Y,000

Y,050

Y,100

Y,150

Y,200

complementary digital group orming (DGF) techniques.DGF processed raw sensor measurements provide aclean group-ormed trace with improved resolutionand low noise.

28. Riedel shears produce a geometric racture patterncommonly associated with strike-slip ault systems.They may orm echelon patterns inclined 10 to 30 tothe direction o motion.

29. Reae AT, Khalil S, Vincent B, Ball M, Francis M,Barkwith D and Leathard M: Increasing Bandwidth orReservoir Characterization with Single-Sensor SeismicData, Petroleum Africa (July 2008): 4144.

30. The nominal old is defned as the number o dierentsource-receiver locations that illuminate a particularsubsurace sampling point or bin. Each o the manysource-receiver pairs, corresponding to a given binlocation, will record reections along dierent raypathsand can be characterized by its nominal azimuth andoset. A broad and uniorm distribution o source-receiver osets and azimuths within each bin providesmore inormation or seismic reservoir characterization.

31. Singh et al, reerence 25.


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shears.28 While this limited study indicated the

presence o numerous structural discontinuities

across the eld that could be related to subseismic

aults or racture corridors, such interpretations

can be validated only through urther integration

o other data sources and ultimately through drill-

ing. An example o validation rom other sources is

the use o ultrahigh-resolution crosswell seismic

imaging (previous page, top right).

To obtain more-detailed inormation about the

ractures in the carbonate reservoirs o Kuwait,

Kuwait Oil Company (KOC) acquired a state-o-

the-art 3D seismic pilot survey over 100 km2

[38 mi2

] o the Northwest Raudhatain eld usingthe WesternGeco Q-Land technology.This system

employs maximum displacement vibroseis sweep

and single-sensor receivers (see Land Seismic

Techniques or High-Quality Data,page 28). The

MD Sweep technique enhances low-requency

content by optimally designing the drive orce and

variable sweep rate o the vibroseis units.29 Single-

sensor deployment enables dense sampling o the

waveeld or removal o source-generated noise.

The advanced acquisition design consisted o

a wide-azimuth square patch, resulting in a very

high nominal old o 990 or 12.5-m by 12.5-m

[41-t by 41-t] bin size with uniorm oset-

azimuth distribution up to 6 km [3.7 mi]. 30 This

design is ideal or seismic racture characteriza-

tion using P-wave data. The Northwest Raudhatain

eld presents an additional challenge because

the seismic refections are contaminated by a

series o multiple-refected seismic waves that

interere with the primary refections over the

reservoir. Advanced data processing is currently

being applied to suppress these multiples and

maximize the extraction o inormation rom the3D seismic data or an extensive seismically

guided racture characterization.

In the past, engineers have proposed that

racture corridors result in early water break-

through but did not have eective tools to detect

their presence. Historically, racture clusters

detected in wellbores were incorporated in sto-

chastic 3D models to explain their eects on pro-

duction. The ability to identiy racture clusters

away rom the wellbore using the FCM workfow

and to visualize their orientation with 3D maps

will help optimize eld development and avoid

unexpected water breakthrough.31

Hydrocarbons from Carbonates

Much o the worlds remaining hydrocarbon

reserves are thought to lie in carbonate rock

whose complexity has oten conounded petro

leum engineers, geophysicists and geologists

working to extract their riches. Step-change

improvements in a wide variety o interpretation

techniques and sensor technologies are making it

possible or these proessionals to more eectivelyevaluate, drill and produce carbonate reservoirs

By integrating techniques and technology, the sta

tistical odds inherent in drilling and maximizing

recovery rom carbonates are being shited in

avor o todays petroleum technologists. TS

>Refning and defning racture clusters. Existing seismic data were processed using discontinuity extraction sotware (DES) models without flters ( left),and the orientation o the ractures is overwhelmingly in line with the axis o the anticlinal structure (NNE-SSW). Logging data rom Wells X-3 and X-4indicated ENE-WSW orientation (insets). This is attributed to Riedel shears caused by NNE-SSW strike-slip aults. Azimuth flters applied to the seismicdata detected racture clusters with dierent orientations (right). The orientation o these clusters is masked in the original processing. (Adapted romSingh et al, reerence 25.)

X-5

X-1

X-2

X-5

X-1

X-3

X-4

X-2

Filters:Search azimuth: All 360Dip angle: Features dip > 70

Filters:Search azimuth: 45 to 135 and 225 to 315Dip angle: Features dip > 70in-line

in-line

45

315225

135

x-linex-line

in-line

in-line

45

315225

135

x-linex-line

X-3

X-3 Dipmeter Data

X-4

X-4 Dipmeter Data

270

90

45

315225

135

0180

270

90

45

315225

135

0180

resolving carbonate complexity- schlumberger

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