Carbonate Complexity Part 2

7/25/2019 Carbonate Complexity Part 2

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48 Oilfield Review

is considered sourceless because once power

which is generated from mud flowing through the

toolis 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 thisfield 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 tools spectroscopy

measurement was able to accurately distinguish

calcite from dolomite and provide the matrix

grain density.

Another common complication encountered in

evaluating deviated wellsespecially in carbon-

atesis 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

Archies saturation equation.

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

(center), but the PEF measurement can be affected by barite in the drillingfluids and by hole conditions. The excessive anhydrite shown in the centertrack is attributed to these effects. If more than two minerals are present,the PEF measurement is less accurate. Spectroscopy that includes amagnesium measurement (right) distinguishes dolomite from calcite and isnot affected by hole conditions and fluid properties. Other minerals can beaccurately 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


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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 tools 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: ComplexCarbonate Reservoir EvaluationA Logging WhileDrilling Field Example, paper AA, presented at theSPWLA Regional Symposium, Abu Dhabi, UAE,April 1618, 2007.

22. Griffiths and Poirier-Coutansais, reference 21.

>Shoulder-bed effects on LWD resistivity measurements. Averaging of resistivity measurements affects the output atbed boundaries. In wells drilled nearly perpendicular to the layering (top left), these effects tend to be localized asthe tool crosses a resistivity interface. Horizontal wells may cross multiple zones with large resistivity contrasts (topright). In this situation, charges accumulate at the interface and induce a polarization horn, or spikeswhich aredependent on the depth of investigationthat are not representative of the actual resistivity ( middle). If notaccounted for during interpretation, the elevated resistivities produce misleadingly high hydrocarbon saturationsusing Archies 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.

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


3/8

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

Archies water saturation equation to match the

sigma-based measurement. Because carbonate

reservoirs often have unknown Rwvalues, simul-taneously solving for water salinity provided a

realistic Rwand wateroutput that satisfied both

equations (above).

Sum Greater than Parts

The 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 aparticular measurement based on its relevance

and the borehole environment to obtain a simul-

taneous solution that includes input from all

measurements.23In 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 Archies equation and sigma saturation measurements. Apparent formation salinity is computed assuming theformation 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 nand mexponents set to 2 and an Rwbased 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, 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 for formation-fluid salinity. Note the lack of separation between deep and shallowresistivities (Track 1) indicating shallow invasion and acceptable sigma measurement. Neutron and density porosities, adjustedfor matrix lithology from spectroscopy data, are also presented (Track 2). (Adapted from Griffiths and Poirier-Coutansais,reference 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


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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 borehole 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 sequencefor analyzing carbonate reservoirs using Carbonate Advisor software.

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

because the grain density reflects the truemineralogy. The porosity difference betweenusing a fixed limestone matrix density value andan effective grain density computed from ECSmineralogy is presented (Track 2, orangeshading). (Adapted with permission of theSPWLA from Ramamoorthy et al, reference 5.)

Anhydrite

Calcite

Dolomite

Illite

Dolomite

Calcite

Anhydrite

Quartz

Bound Water

Porosity Correction

23. Ramamoorthy et al, reference 5.

>Porosity partitioning of NMR data. The distribution of T2transverse relaxation time data (Track 1) fromthe NMR tool is partitioned based on cutoffs that can be refined from core analysis. In this examplevolumes computed from distributions to the left of the red line (Track 1) represent microporosity, whichcorrespond to the blue shaded volume in Track 2. Microporosity measurements from core are plottedalong with the microporosity volume for confirmation. 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 of the blue line). Permeability from core data isplotted with permeability computed from NMR data (Track 3). The free-fluid volume computed fromNMR data can be similarly partitioned (Track 4). Fluid volume to the right of the cutoff (blue line) isassociated with mesoporosity, and the volume to the left is macroporosity. Core data points agree withcomputed 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 Macroporosity

30 % 0

Macroporosity Cutoff

30 % 0

Free Fluid, NMR

Microporosity

Mesoporosity

MacroporosityT2Distributions

T2Cutoff Short

T2Cutoff Long


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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.24Because

the NMR data provide pore size from T2distribu-

tions, assuming bulk and diffusion effects are

minimal, by integrating the T2distribution, a cap-

illary pressure versus saturation relationship can

be developed. To convert T2data 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 Advisorprogram. These outputs provide an integrated and comprehensiveevaluation of the key properties that describe a reservoirs storage and flowcapacity. The petrophysicist may weight the data from specific tools andchoose between tools (Depth track, AIT array induction imager tool, green;and HRLA high-resolution laterolog array, gold). Complex lithology and fluidvolumes (Track 1) are shown along with a moved-hydrocarbon analysis(orange) from microresistivity data. Fluid-flow models are constructed fromresistivity data (Track 2). Porosity from NMR data (Track 3) are partitionedand the results graphically displayed (Track 4). A full ternary analysis (Track 5)

is useful for identifying better quality reservoir rock. Drainage capillarypressures are computed from 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 filled atlow capillary pressure. The shading transitions from blue to red,corresponding to successively higher capillary pressures required to filladditional 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 from Ramamoorthy et al, reference 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

T2Distributions

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


6/8

Summer 2010 53

Searching Above Ground

Approaches 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 fractureswarms 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-surfacegener-

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-

turedfrom 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 wel

above 10 darcies. These corridors often act as

major conduits for fluid flowing within the reser

voir and may be responsible for early wate

breakthrough from natural drive or waterflood

ing. Therefore, to manage field production effec

tively and maximize total recovery, it is crucia

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 T2Relaxation to Drainage Capillary Pressure in VuggyCarbonate Reservoirs, paper SPE 101897, presented atthe SPE Annual Technical Conference 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 fromMiddle East Carbonates, First Break26, no. 5(May 2008): 109113.

>Multiscale seismically constrained fracture characterization. Fracturesmay exist over a wide range of scales from very small cracks to very largefaults. Understanding their distribution and properties at these differentscales is essential to characterize naturally fractured 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 fractures include layer-bounddiffuse fractures that can pervade across a geologic layer and arefrequently observed in image logs such as those from the FMI fullboreformation microimager. Typically, these fracture types are the primarycontrols used to build geologic models containing fractures, such as implicitfracture models or discrete fracture networks (DFN). Although these diffusefractures are smaller than surface seismic wavelengths, a large populationdensity of such fractures can be detected with seismic measurements by

analyzing the seismic anisotropy. Mesoscale fracture corridors andsubseismic faults are the most difficult scale of fractures to characterize;

they are at the lower end of surface seismic resolution and few wells mayintersect them. These narrow features cross layer boundaries and, withsuitable 3D seismic data and careful analysis such as with the fracturecluster mapping workflow, they can be detected as subtle discontinuities inthe data. Because mesoscale fracture corridors can have very highpermeabilities and have major influence over reservoir dynamics, theyshould be incorporated into geologic models as individual fracture patchsets. In contrast to micro- and mesoscale fractures, macroscale faults arecomparatively easy to detect with 3D seismic data and form the basis forstructural modeling. Computer interpretation methods for fault detection,such as the ant tracking algorithm used in the Petrel seismic-to-simulationsoftware, are available to automate the process and may be able toovercome analyst bias. Detailed analysis of the seismically derived rock

properties around these faults may help in assessing fault 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


7/8

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.26To focus on detecting frac-

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-

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 Well

The 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-acquisitiongeometry and distort amplitude and phase of reflections.This form of noise can obscure true subsurfacereflections and should be removed prior tointerpretation, if possible. Although the FCM workflowmight detect them, an experienced interpreter shouldbe able to identify them as noise rather than fractures.

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

>Surface relief map of Sabriyah field in northernKuwait. This field, the first of five to be analyzed,was considered a key area in the study.Geoscientists used the FCM workflow to evaluateexisting seismic data. Wells X-5 and X-6 were tobe drilled based on study results. Boreholeimages and core from these wells validated thefracture 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 surface seismic data (left) can resolve featuresdown to tens of meters. Crosswell imaging, suchas the DeepLook-CS seismic imaging service,

acquires data from downhole sources andreceivers placed in separate wells. Using higherfrequencies extending to kilohertz providesultrahigh-resolution images between wells andcan resolve features as small as 1.5 m [5 ft]. Seenin the crosswell data (right) is a subseismic fault(magenta line) and the detailed multilayeredreservoir structure. Fracture corridors, interpretedfrom discontinuities detected in a 3D seismicvolume, can also be verified from this type ofcrosswell seismic imaging.

X,950

Depth,ft

Y,000

Y,050

Y,100

Y,150

Y,200

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

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

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

30. The nominal fold is defined as the number of differentsource-receiver locations that illuminate a particularsubsurface sampling point or bin. Each of the manysource-receiver pairs, corresponding to a given binlocation, will record reflections along different raypathsand can be characterized by its nominal azimuth andoffset. A broad and uniform distribution of source-receiver offsets and azimuths within each bin providesmore information for seismic reservoir characterization.

31. Singh et al, reference 25.


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Summer 2010 55

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 usingthe 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.29Single-

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]. 30This

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 the3D 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 Carbonates

Much of the worlds remaining hydrocarbon

reserves are thought to lie in carbonate rock

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 i

possible for these professionals to more effectivelyevaluate, 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 todays 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-4indicated ENE-WSW orientation (insets). This is attributed to Riedel shears caused by NNE-SSW strike-slip faults. Azimuth filters applied to the seismicdata detected fracture clusters with different orientations (right). The orientation of these clusters is masked in the original processing. (Adapted fromSingh et al, reference 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

Documents

Carbonate Complexity Part 2