doi:10.1144/SP292.7 2007; v. 292; p. 123-136 Geological Society, London, Special Publications

 L. A. Valcke, M. Casey, G. Lloyd and W. Ben Ismail J.-M. Kendall, Q. J. Fisher, S. Covey Crump, J. Maddock, A. Carter, S. A. Hall, J. Wookey, S. 

siliciclastic rocksSeismic anisotropy as an indicator of reservoir quality in 

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Seismic anisotropy as an indicator of reservoir quality in

siliciclastic rocks

J.-M. KENDALL1, Q. J. FISHER2, S. COVEY CRUMP3, J. MADDOCK2, A. CARTER2,4,

S. A. HALL3, J. WOOKEY1, S. L. A. VALCKE2,6, M. CASEY2, G. LLOYD2 &

W. BEN ISMAIL4

1Department of Earth Sciences, University of Bristol, Wills Memorial Building, Queen’s Road,

Bristol BS8 1RJ, UK (e-mail: gljmk@bristol.ac.uk)2School of Earth and Environment, Institute of Geophysics and Tectonics, University of Leeds,

Leeds LS2 9JT, UK3School of Earth, Atmospheric and Environmental Sciences, University of Manchester,

Williamson Building, Oxford Road, Manchester M13 9PL4CNRS, Laboratoire 3S-R, Grenoble, France

5Now at: StatoilHydro Research Centre, Arkitekt Ebbellsvei 10, Rotvoll,

N-7005 Trondheim, Norway6Now at: Faculty of Geosciences, Utrecht University, P.O. Box 80021, 3508 TA Utrecht,

The Netherlands

Abstract: Improving the accuracy of subsurface imaging is commonly the main incentive forincluding the effects of anisotropy in seismic processing. However, the anisotropy itself holdsvaluable information about rock properties and, as such, can be viewed as a seismic attribute.Here we summarize results from an integrated project that explored the potential to use obser-vations of seismic anisotropy to interpret lithological and fluid properties (the SAIL project).Our approach links detailed petrofabric analyses of reservoir rocks, laboratory based measure-ments of ultrasonic velocities in core samples, and reservoir-scale seismic observations. Wepresent results for the Clair field, a Carboniferous–Devonian reservoir offshore Scotland, westof the Shetland Islands. The reservoir rocks are sandstones that are variable in composition andexhibit anisotropy on three length-scales: the crystal, grain and fracture scale.

We have developed a methodology for assessing crystal-preferred-orientation (CPO) using acombination of electron back-scattered diffraction (EBSD), X-ray texture goniometry (XRTG)and image analysis. Modal proportions of individual minerals are measured using quantitativeX-ray diffraction (QXRD). These measurements are used to calculate the intrinsic anisotropydue to CPO via Voigt-Reuss-Hill averaging of individual crystal elasticities and their orientations.The intrinsic anisotropy of the rock is controlled by the phyllosilicate content and to a lesser degreethe orientation of quartz and feldspar; the latter can serve as a palaeoflow indicator. Our resultsshow remarkable consistency in CPO throughout the reservoir and allow us to construct a mathe-matical model of reservoir anisotropy. A comparison of CPO-predicted velocities and thosederived from laboratory measurements of ultrasonic signals allows the estimation of additionalelastic compliance terms due to grain-boundary interactions. The results show that the CPO esti-mates are good proxies for the intrinsic anisotropy of the clean sandstones. The more micaceousrocks exhibit enhanced anisotropy due to interactions between the phyllosilicate grains. We thencompare the lab-scale predictions with reservoir-scale measurements of seismic anisotropy, basedon amplitude variation with offset and azimuth (AVOA) analysis and non-hyperbolic moveout.Our mathematical model provides a foundation for interpreting the reservoir-scale seismic dataand improving the geological modelling of complex reservoirs. The observed increases inAVOA signal with depth can only be explained with an increase in fracturing beneath themajor unit boundaries, rather than a change in intrinsic CPO properties. In general, the styleand magnitude of anisotropy in the Clair field appears to be indicative of reservoir quality.

Seismic methods provide the best tools for imagingthe architecture of structurally-complex petroleumreservoirs. Equally important is the relationship

between such structure and the litho-stratigraphicproperties, stress-state and the fluid response tochanges within the reservoir. Recent advances in

From: JOLLEY, S. J., BARR, D., WALSH, J. J. & KNIPE, R. J. (eds) Structurally Complex Reservoirs.Geological Society, London, Special Publications, 292, 123–136.DOI: 10.1144/SP292.7 0305-8719/07/$15.00 # The Geological Society of London 2007.

acquisition and processing have seen the develop-ment of a range of seismic techniques for inferringsuch reservoir properties. These include time-lapseseismic surveys, converted-wave and shear-wavesurveys, and passive seismic monitoring, to namea few.

Seismologists are now better equipped to imagecomplex structures that are not only heterogeneousbut also anisotropic. Whereas seismic heterogeneityrefers to spatial variations in velocity, seismic ani-sotropy refers to variations in velocity with direc-tion of propagation. For example, horizontallytravelling P-waves in shales are usually faster thanvertically travelling P-waves (e.g. Thomsen 1986).This style of anisotropy is commonly referred toas vertical transverse isotropy (VTI), which is aclass of hexagonal symmetry with a vertical sym-metry axis and can be described by five independentparameters (e.g. the Thomsen (1986) parameters).In contrast, azimuthal anisotropy can be detectedby analysing azimuthal variations in stacking velo-cities, amplitudes (including those of convertedwaves), and polarization anomalies. An exampleof azimuthal anisotropy is horizontal transverse iso-tropy (HTI), hexagonal symmetry with a horizontalsymmetry axis. Another indicator of anisotropy isthe propagation of two independent shear-wavesthat travel at different speeds, which is commonlyreferred to as shear-wave splitting because the twoshear-waves separate as they propagate.

A number of methods have been developed forestimating seismic anisotropy using a wide rangeof seismic data attributes. Commonly, the emphasisis on improving the sharpness of seismic images(Verwest 1989; Bouska & Johnston 2005) orbetter reconciling variations in depth estimatesderived from various datasets (e.g. boreholeversus surface seismic data; Banik 1984; Peng &Steenson 2001; Brandsberg-Dahl & Barkved2002). However, the anisotropy itself is an indicatorof a wide range of properties and as such is aseismic attribute that may be influenced by bothpast and present geological processes. A key chal-lenge with its interpretation is untangling thevarious contributions to the anisotropy. The SAILproject (seismic anisotropy as an indicator of lithol-ogy) was designed to address this issue. This was anITF (Industry Technology Facilitator UK) coordi-nated project that was funded by eight oil compa-nies and the UK Department of Trade andIndustry (DTI). The aim of the consortium was toinvestigate the use of observations of seismic aniso-tropy as an indicator of lithology, fluid propertiesand rock fracture.

It has been long recognized that the Earth is seis-mically anisotropic on length scales ranging fromthe crystal to the craton. Most crystals exhibitstrong elastic anisotropy, including cubic crystals

such as halite. Crystal preferred orientation (CPO)(or lattice preferred orientation; LPO) withinrocks is an effective way of generating anisotropyand it can be caused by depositional processes andsubsequent deformation (Silver 1996).

Anisotropy will also result from the preferredorientation of shaped inclusions, grain boundariesor layers, and this is often referred to as a shapepreferred-orientation (SPO). Such shape fabric andgrain orientations are commonly observed in handspecimens and at the outcrop scale. In general, flatdisc-shaped inclusions are more effective thanelongate cigar-shaped inclusions at generatinganisotropy (Kendall 2000). The inclusions can behigher or lower in velocity than the surroundingmatrix. For example, aligned fluid-filled ellipsoidalpores or micro-cracks are very effective at generat-ing anisotropy (e.g. Crampin 1994). Furthermore,the periodic layering of materials with contrastingelastic properties will generate transverse isotropyif the seismic wavelength is much longer than thelayer thickness (Backus 1962). For example,alternating bands of high and low porosity rockwill appear seismically anisotropic. At a largerscale, the preferred alignment of fractures or jointsets will also lead to seismic anisotropy, as longas the fracture spacing is on a smaller length-scalethan the dominant seismic wavelength. Theseismic wavelength is on the order of 100s ofmetres for typical reservoir depths, so the fracturespacing must be on the order of less than tensof metres.

This article considers the seismic anisotropy insiliciclastic reservoir rocks from the Clair field,which lies west of the Shetland Islands and is thelargest petroleum discovery on the UK continentalshelf (Ridd 1981; Coney et al. 1993). Although itis one of the largest fields in UK territory, develop-ment has only recently started because reservoirheterogeneity has made it difficult to assess accu-rately future hydrocarbon production based on theresults from the discovery and subsequent appraisalwells. As with many fields, fractures seem toincrease reservoir connectivity in Clair and shouldtherefore increase production rates (Smith &McGarrity 2001). There is mounting interest inintegrating seismic methods, well test data andgeological modelling to access fracture densityand orientation distribution as well as productionbehaviour (Barr et al. 2007). A range of seismictechniques are available for measuring anisotropyand potentially fracture orientations/distributionsincluding: shear-wave splitting in VSP data(Winsterstein 1989) and surface seismic data(Gaiser et al. 2001), amplitude variations with offsetand azimuth (AVOA) (Lynn & Thomsen 1990; Hall& Kendall 2003), and converted wave amplitudevariations with azimuth (Granger et al. 2000).

J.-M. KENDALL ET AL.124

In this paper we explore controls on seismic ani-sotropy over a range of length-scales in the Clairreservoir rocks within the context of the SAILworkflow, which is shown in Figure 1. Ourapproach links detailed petrofabric analyses ofreservoir rocks, laboratory-based measurements ofultrasonic velocities in core samples, and field-scaleseismic anisotropy observations. Firstly, detailedpetrofabric analysis is used to quantify the modalproportions of minerals and the orientation of indi-vidual crystals. This allows the development of a‘geomathemical’ model of the rocks’ intrinsic

anisotropy due to CPO. This model provides thebase to which larger scale anisotropy can beadded, for example, that due to oriented fracturesets. The second part of the study involves ultra-sonic velocity measurements on core samples. Themeasurements were carried out for a range of con-fining pressures, which in turn allowed an assess-ment of the degree to which the CPO anisotropycould explain the measured anisotropy. Part of themotivation for the velocity measurement wasto quantify the degree of additional anisotropy dueto grain-scale interactions. For example, more

INTRINSIC ANISOTROPYLPO

EXTRINSIC ANISOTROPYSPOStress−induced crack/fractures

SEISMICSAVOAShear−wave splittingNon−hyperbolic Moveout

EBSD/XRTG

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Image Analysis

GEOMATHEMATICAL MODELModel CijsBingham/March model for MicaUpscalingDamage Model for Fractures

ULTRASONICS

SAIL WORKFLOW

RFIAMS

RESERVOIR INTERPRETATION

Fig. 1. The workflow adopted for the SAIL project. The intrinsic CPO anisotropy (or lattice preferred orientation(LPO)) is determined from petrofabric analyses using quantitative electron back-scattered diffraction (EBSD), X-raytexture goniometry (XRTG) and image analysis. Modal proportions for each mineral are determined using quantitativeX-ray diffraction (QXRD). Seismic velocities are measured in the laboratory using ultrasonic transducers. These stepsallow the development of a geomathematical model for the intrinsic anisotropy. Longer-wavelength anisotropy can bethen added to this model using effective medium modelling. Anisotropic magnetic susceptibility (AMS) is onetechnique for estimating shape-preferred orientations (SPO) due to aligned cracks and pores. Comparisons of theultrasonic measurements and the intrinsic anisotropy can be used to estimate stress-induced anisotropy due to alignedcracks and intergranular effects (extrinsic anisotropy). Analysis of field-scale seismic data provides estimates ofanisotropy using a variety of techniques (amplitude variation with offset and azimuth (AVOA), shear-wave splitting,and non-hyperbolic moveout). Cumulatively, these results and predictions can be used to help guide interpretations ofrock and reservoir properties. RFI (robust fracture interpretation) refers to a related ITF project that considered the linkbetween seismic anisotropy and geomechanical properties of reservoirs.

SEISMIC ANISOTROPY AS AN INDICATOR OF RESERVOIR QUALITY IN SILICICLASTIC ROCKS 125

compliant material between the harder grains canlead to SPO anisotropy (e.g. Sayers 2002).Finally, a 3D ocean-bottom seismic dataset is ana-lysed using AVOA analysis in an attempt to mapout spatial variations in fracture-induced aniso-tropy. We adopt the methodology of Hall &Kendall (2003) as, like most OBS datasets, theClair data are sparse in offset and azimuth coverage.This integrated workflow permits evaluation of thedegree to which the AVOA is due to fractures asopposed to other competing and more intrinsicmechanisms such as CPO.

Crystal-scale anisotropy

A suite of rocks from a range of reservoir depthswere collected from core of two wells near thecentre of the Clair field. 20 samples were collectedto cover the full range of lithologies present.Sample depths ranged from 1660 m to 2200 m.The rocks vary from clean medium-grained sand-stones to fine-grained laminated mudstone. Ingeneral, the reservoir quality improves as the sand-stones become cleaner (less clay-rich) towards thebottom of the reservoir (Coney et al. 1993;Maddock 2006).

Mineralogical analysis was carried out usingquantitative X-ray diffraction (QXRD) (Hillier

1999). The dominant minerals in the samples arequartz, feldspar, calcite, biotite, muscovite, andfine-grained clays such as illite, kaolinite andsmectite. Figure 2 shows an example of variationsin the modal proportions of the rock samples thatwere also used for velocity analysis in the laboratory.Porosity was determined using mercury injectionporosimetry and shows that the porosity of therocks varies from 7% in the mudstones to over15% in the cleaner sandstone.

CPO has been long recognized as an importantmicrostructural control on seismic velocities (e.g.Kaarsberg 1959), but there has been little workthat quantifies this effect in sedimentary rocks. Anotable exception is the work of Hornby and co-workers (Hornby et al. 1994; Hornby 1998) whoused laboratory velocity testing, image analysisand effective medium modelling to understand theelastic properties of shales better. CPO in sedimen-tary rocks is primarily controlled by gravitationaland mechanical compaction. Elongate or platy min-erals tend to align and have crystal structure thatmimics the grain shape (e.g. clays and micas).Other effects are the diagenetic growth of minerals(Archibald et al. 1996) or depositional alignmentdue to strong currents (Pettijohn 1975).

The characterization of the crystal preferredorientation (CPO) is performed using the method-ology developed in the SAIL project (see Valcke

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Fig. 2. Modal variations in minerals, as determined from QXRD, and porosity, as determined from mercuryimpregnation, in the 9 Clair specimens that were subjected to laboratory velocity measurements at confining pressuresup to 50 MPa. Petrofabric analysis was carried out on a total of 20 samples from two wells and sample the range oflithologies present in the reservoir.

J.-M. KENDALL ET AL.126

et al. 2006 and Maddock 2006). Electron back-scatter diffraction (EBSD) was used primarily tocharacterize the orientation of quartz, feldspar andcalcite crystals. This technique indexes eachcrystal and measures their orientations. This is thefirst time (to the authors’ knowledge) that EBSDhas been used for sedimentary rocks. EBSD is lesseffective with phyllosilicates due their platynature and the fact that they are often very finegrained. Instead, X-ray texture goniometry(XRTG) (Van der Pluijm et al. 1994) and imageanalysis can be used to assess CPO in micas andclays. A key difference between EBSD andXRTG image-analysis is that the former producespoint measurements for each crystal, whereas thelatter measures the bulk aggregate CPO of aparticular mineralogy.

The EBSD measurements for the quartz andfeldspar show remarkably consistent orientationsin all of the Clair reservoir rocks. FollowingValcke et al. (2006), their orientations are inter-preted as palaeoflow indicators and their orien-tations agree with other independent Clair studiesof palaeoflow (Hailwood pers comm.). Such

alignments have been seen in other studies (e.g.Pettijohn 1975; Baas 2000). In contrast, the calcite/dolomite crystals show a random orientation. Thisis due to their diagenetic origin; they grow inpore spaces as fluids circulate through the rock. Inother studies, calcite commonly shows a preferredorientation when it grows along mica substrata (seeValcke et al. 2006) rather than in pore spaces.

Image analyses and XRTG were used to assessCPO in micaceous components of the samples.Figure 3 shows an electron back-scatter atomic-contrast image of a mica-clay rich specimen.These images support assumptions that the micaCPO is due to compaction and has an axial sym-metry around a vertical axis.

Fine-grained authigenic clays are assumed to berandomly orientated as they are precipitated withinthe pore spaces of the rock. They are difficult toimage and the elasticities of such clays are poorlyknown (Jakobsen et al. 2003). The elastic constantsof Katahara (1996) are used for the fine-grainedclays. Initially, we assumed that the pores werespherical in shape and randomly distributedthrough the rock.

Fig. 3. Back-scatter electron photomicrogaph of a typical Clair sandstone (bottom) and a typical Clair mudstone (top).The sandstone is well-sorted, shows moderate porosity and contains authigenic calcite. In constrast, the mudstone ispoorly sorted, shows low porosity and contains well aligned phyllosilicates.

SEISMIC ANISOTROPY AS AN INDICATOR OF RESERVOIR QUALITY IN SILICICLASTIC ROCKS 127

A geomathematical description of

anisotropy

The aggregate anisotropy in a rock due to CPO canbe estimated given the modal mineralogy, theelastic constants of the minerals and the orientationsof the crystals. We use this to produce a ‘geomathe-matical’ model description of the anisotropy in theClair reservoir rocks. The EBSD measurementsdescribed above are used to assess the anisotropydue to quartz and feldspar CPO. The alignment ofmicas (muscovite, biotite and chlorite) as assessedusing XRTG can be described by a statistical distri-bution or orientation distribution function. The iso-tropic elastic properties of the fine clays, calcite andporosity show no coherent preferred orientation, butit is important to include these isotropic com-ponents as they dilute the overall CPO anisotropy.These various contributions can be then summedusing modal-volume-weighted Voigt-Reuss-Hill(VRH) averaging (Hill 1952).

EBSD results define the CPO for each crystalusing three Euler angles, which can be then usedto rotate the single-crystal elastic constants into auniversal coordinate system. Voigt-Reuss-Hillaveraging is then used to evaluate the effective ani-sotropy in a rock due to a given mineralogy (Hill1952; Mainprice 1990). Finally, the aggregate ani-sotropy, or elastic constants, of the rock are deter-mined by VRH averaging of each mineralconstituent with a QXRD determined weightingfor the modal proportion for each mineral.

We used a Bingham (1974) distribution todescribe the uniaxial probability distribution ofthe micas, which characterized the strength of thepreferred orientation. This in turn can be used toassess the average elastic stiffnesses (Cij) dueto mica alignment. Where possible we used EBSDto benchmark these models (Maddock 2006). Thecontribution to the anisotropy from the micas wasthen included as part of the volume fractionweighted VRH average for the whole rock.

Figure 4 shows the predicted anisotropy due tothe micas, quartz and feldspar. These diagramsshow lower hemispherical projections of theP-wave velocities and the shear-wave splitting(horizontal wave propagation directions areplotted at the edge of the circle and velocities forvertically propagating waves lie in the centre ofthe circle). If a rock were composed entirely ofmicas, the P-wave anisotropy would be over 40%and the maximum amount of shear-wave splittingwould be nearly 40%. The mica anisotropy has aVTI symmetry due to the assumption of uniaxialsymmetry and the first-arriving shear-wave will bepolarized in the horizontal plane. In contrast, theanisotropy due to quartz alignment has an

orthorhombic symmetry and predicts much lowerlevels of anisotropy (,2% for P-waves and amaximum of nearly 2% shear-wave splitting). Feld-spar orientations show very similar symmetry, buthigher amounts of anisotropy (almost 7% forP-waves and a maximum of nearly 7% shear-wavesplitting). As mentioned, the orientation of thequartz and feldspar appears to be an indicator ofpalaeoflow and is roughly consistent for all of therocks sampled throughout the reservoir.

Figure 5 shows the combined geomathematicalmodel results for a mica-rich rock and one for acleaner sandstone. These results include theeffects of mica, quartz and feldspar alignment andthe diluting effects of the randomly-oriented fineclays, diagenetic calcite, and porosity. It is clearfrom comparisons with Figure 4 that the anisotropicsymmetry in the clean sandstone is dominated bythe quartz and feldspar. The more anisotropicmica-rich rock shows a nearly VTI anisotropyindicating the dominant influence of the micasymmetry.

Although the overall aggregate amount of aniso-tropy varies between samples, the strength of aniso-tropy due to each mineral is consistent (seeMaddock 2006 for detail). This means that oneonly needs to know the volume fractions of eachmineral to assess the anisotropy for a particularspecimen via the stiffness tensor (i.e. Cij). Theseresults provide a basis for a mathematical modelof the intrinsic elasticity of Clair reservoir rocks.

The results obtained suggest significant vari-ations in both the magnitude (Fig. 6) and the sym-metry of CPO anisotropy throughout the Clairreservoir. The mica content largely dictates themagnitude of the anisotropy in these rocks andthere is a transition from VTI to orthorhombic sym-metry as the magnitude of the anisotropy decreasestowards the bottom of the reservoir.

Seismic waves are not very sensitive to thesmall-scale variations in elastic properties fromsample to sample. Therefore for our geomathemati-cal model of the Clair field anisotropy, we deter-mine a single VRH average anisotropy for each ofthe major lithological units bounded by majorseismic reflection horizons (see magnitude vari-ations in Fig. 6 and Unit VI symmetry in Fig. 7).

Any interpretation of anisotropy from seismicmethods must first account for the contributionfrom the CPO effect. Using effective mediummodels (e.g. Hudson 1981), the effects of cracksor fractures can be superimposed on the CPOanisotropy. Figure 7 shows the effect ofvertically-aligned fractures on the overall aniso-tropy from the average rock in Unit VI. A crackdensity of 0.05 and an aspect ratio of 0.001 isused in the calculation. The magnitude of the

J.-M. KENDALL ET AL.128

anisotropy does not change significantly. However,the symmetry of the anisotropy is now significantlynon-VTI leading to azimuthal variations in thevelocities that could produce observable AVOA

effects. In contrast, the VTI symmetry in theuncracked rock will not produce an AVOA signalas there is little azimuthal variation in velocity(and hence reflection coefficient).

Fig. 4. Anisotropy due to the alignment of (a) quartz, (b) feldspar, and (c) mica in the Clair specimens. The firstcolumn shows P-wave velocities as a function of direction (lower-hemisphere projection). The centre of the hemispherecorresponds to vertical wave propagation, and the perimeter of the hemisphere shows azimuthal variations in horizontalvelocities (normally those in the bedding plane). The maximum and minimum P-wave velocity and the P-waveanisotropy (100 � (Vmax 2 Vmin)/Vave) are also given below each hemisphere in this column. The second and thirdcolumns show variations in shear-wave splitting on a lower-hemisphere projection. Shear-wave splitting is expressed asa percentage and is defined as [100 � (Vsfast 2 Vsslow)/Vsave] for a given direction of wave propagation. The maximumand minimum splitting are given below the middle column hemispheres. The ticks on the hemispheres in the rightcolumn show the polarizations of the leading (fast) shear-wave for a given direction of wave propagation.

SEISMIC ANISOTROPY AS AN INDICATOR OF RESERVOIR QUALITY IN SILICICLASTIC ROCKS 129

Laboratory velocity measurements and

grain-scale anisotropy

As described in the previous section, the fine-scaleCPO anisotropy can be overprinted by larger-scaleSPO anisotropy. For example, alternating beds ofhigh and low porosity sediment or aligned ellipsoi-dal pores can be effective in generating anisotropy.At a much larger scale, fractures that extend tens ofmetres and serve as fluid permeability pathwayswill also be very effective at generating anisotropyin seismic data, as long as the wavelength of theseismic waves is much larger than the spacingbetween fractures.

To address the finer-scale SPO anisotropy, acomponent of the SAIL project involved laboratorymeasurements of seismic velocities using ultrasonictransducers. Ultrasonic measurements were madeas a function of confining pressure for both P- andS-waves in a purpose-made rig.

Grain-scale contributions to the anisotropy canbe assessed through differences between the pre-dicted anisotropy due to CPO and the anisotropy

measured in a sample using ultrasonic seismicvelocities. Details of this approach are presentedin Hall et al. (2007) which draws on the earlierwork of Sayers (2002). This methodology allowsthe determination of crack density tensors thatdescribe the combined effects of multiple popu-lations of microcracks, crack-like porosity andintergranular effects.

The results show that the extrinsic intergranularanisotropy in the core samples is strongly related tothe intrinsic CPO anisotropy (see Hall et al. 2007).The effect is strongest when the rocks are at roompressure. As confining pressures increase micro-cracks close. However, even at 50 MPa a substan-tial residual non-CPO induced anisotropy remains,which is generally aligned with the symmetry ofthe CPO anisotropy. Furthermore, a strong link isobserved between the measured degree of velocityanisotropy and the mica content, which cannot beexplained by the CPO effect alone. This residualcomponent could be due to the presence of morecompliant material lying between the faces of thestiffer mica grains. This effect can be described in

Fig. 5. P-wave anisotropy and shear-wave splitting in (a) a mica-rich sample, where the mica dominates theanisotropy, and (b) a mica-poor sample, where the quartz and feldspar anisotropies dominate. See Figure 4 caption forfurther explanation of anisotropy plots.

J.-M. KENDALL ET AL.130

terms of microcracks filled with relatively softmaterial.

Figure 8 shows the separated CPO and micro-crack components of the velocity anisotropy andthe cumulative velocity anisotropy as functions ofmica content. The CPO anisotropy increases withmica content, as might be expected. However, thecrack-induced anisotropies also show a generalincrease with mica content, especially for thesamples with more HTI symmetries. Furthermore,the two components of the anisotropy are roughly

aligned. Therefore, it appears that the micas notonly contribute strongly to the CPO-induced aniso-tropy, but they also have a strong influence on thegrain-scale microcrack anisotropy. In fact, theresults suggest that the grain-scale microcracksdeveloped preferentially in planes parallel to thecleavage planes of the flat mica grains.

Fracture-scale anisotropy

In 2002, BP acquired a large, 3D 4-componentocean-bottom dataset over an extensive area of theClair field (Kommedal et al. 2005). A componentof the SAIL project involved the analysis of azi-muthal variations in P-wave reflection amplitudesusing part of this dataset.

Vertically-aligned fractures can produce azi-muthal variations in AVO (AVOA) (e.g. Ruger1998). In this context, azimuthal variation in theAVO gradient is approximately elliptical for nearoffsets; the orientation and ellipticity of the AVOgradient ellipse are useful attributes for fracturecharacterization. We adopt the methodology ofHall & Kendall (2003) for quantifying the AVOAsignal. A notable difference in this study is theuse of trim statics, which enable better tracking ofevents across 3D CMP gathers.

The results of the AVOA analysis are shown inFigure 9. In general, the dominant orientation of themajor axis of the AVOA ellipse is on average 1388(with a standard deviation of 46), which agrees withindependent estimates of fracture orientations fromcore analysis and VSP data (Smith & McGarrity2001; Coney et al. 1993; Barr et al. 2007).Figure 10 shows a ‘quiver plot’ mapping the magni-tude and orientation of the AVOA results (seecaption) for reflections from the top of Unit V.This plot shows that although the average fractureorientation is roughly NW–SE there is considerablevariability in the strength and orientation on a finescale. Coherent regions of high anisotropy shareborders with relatively isotropic regions (i.e. anabsence of fractures). An advantage of the AVOAstudy over borehole-based studies is the addedspatial coverage that comes from using surfaceseismic data.

Figure 9 also shows that the mean magnitude ofthe anisotropy increases from the top of the reser-voir (BCU reflection) to the bottom (reflectionfrom the Lewisian basement rock). The CPO aniso-tropy predicts a VTI symmetry near the top of thereservoir, but in contrast predicts the magnitude ofthe anisotropy to decrease towards the base of thereservoir. At face value this suggests that theAVOA anisotropy is due to a more extrinsic mech-anism and fractures are the likely cause. However,there is some ambiguity inherent in the AVOA

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SEISMIC ANISOTROPY AS AN INDICATOR OF RESERVOIR QUALITY IN SILICICLASTIC ROCKS 131

Fig. 7. (a) VRH-average anisotropy in Unit VI due to CPO. (b) Anisotropy in Unit VI where vertically aligned cracks,oriented in a left-to-right direction across the page, are superimposed on the intrinsic CPO anisotropy. The cracks donot significantly change the magnitude of the anisotropy, only the symmetry of the anisotropy. The uncrackedsample has a nearly VTI symmetry, whereas the cracked sample has a nearly orthorhombic symmetry.

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Mica modal proportion (%)

(c) Total anisotropy

Fig. 8. P- and S-wave anisotropy as a function of mica content: (a) anisotropy due to CPO alone (pressure invariant);(b) anisotropy at 45 MPa due solely to crack and intergranular effects, based on the crack densities inverted fromthe measured velocities (with the CPO anisotropy taken into account) and the Voigt-Reuss-Hill averaged isotropicelasticities (see Hall et al. 2007); (c) the combined effects of CPO and intergranular anisotropy at 45 MPa. Eachline corresponds to comparisons of different directions (e.g. vpx 2 vpy is the difference in velocities of P-wavespropagating in the x and y directions; vsxy 2 vsxz refers to shear-waves propagating in the x direction and shows thedifference in the velocities of those polarized in the y direction versus those polarized in the z direction). The xdirection is orientated parallel to the bedding strike.

J.-M. KENDALL ET AL.132

interpretation as to whether or not the anisotropyincreases or decreases across an interface.

A means of testing whether or not the predictedVTI anisotropy is coherent at the seismic scale is toestimate anisotropy parameters from non-hyperbolic moveout (Alkhalifah & Rampton 2001;Tsvankin & Thomsen 1994; Van der Baan &Kendall 2002). Unfortunately, the long offset arri-vals required for such analysis were unclear in thereal data (preliminary analysis using short offsetdata suggested a decrease in VTI anisotropytowards the bottom of the reservoir, but thisdeserves further investigation; Ramptonpers.comm.).

The geomathematical model of the reservoiranisotropy was used to interpret the AVOA resultsbetter. The elastic constants are averaged (using

VRH) for a particular geological unit above andbelow the major interfaces whose reflections wereused in the AVOA analysis. Varying levels of ver-tical fractures are added to the anisotropic unitsabove and below the interface. A reflectivity-based modelling approach (Thomson 1997), whichallows fully anisotropic layers, is then used topredict the AVOA response. Figure 11 shows thepredicted AVOA magnitude (Hall & Kendall2003) for the interface between Units V and VI.The strongest AVOA response is observed whenthe fracturing increases moving downwards fromUnit VI to Unit V. In general, the AVOA magni-tudes observed with the real data exceed a dimen-sionless value of 0.2. The CPOþ crack basedpredictions can only be this large if the fracturingincreases below the boundary. The AVOA due to

Fig. 9. A compilation of the AVOA results for reflections from the base-Cretaceous unit (BCU), top of Unit VI, top ofUnit V and the Lewisian gneissic basement. The left column shows spatial variations in magnitude of the AVOAanisotropy (the normalized difference between the maximum and minimum AVO gradients). The middle columnshows rose diagrams of the inferred fracture orientations. The far right column shows the average magnitude of theAVOA anisotropy for each reflector; note the increase in anisotropy with depth of the reflector.

SEISMIC ANISOTROPY AS AN INDICATOR OF RESERVOIR QUALITY IN SILICICLASTIC ROCKS 133

CPO anisotropy is far too weak to explain theobserved signals.

As fractures increase permeability within Clair,the identification of an increase in fracturing fromUnit VI to Unit V may be used to infer an increasein permeability. In other words, changes in seismicanisotropy derived using AVOA analysis has pro-vided an indication of the distribution of high per-meability layers within the Clair reservoir.

Conclusions and future directions

The SAIL project has brought together geologicalanalyses, rock physics and geophysical investi-gations of anisotropy in the Clair field. In thecourse of this work, the project has consideredanisotropy on a range of length scales from thecrystalline to the grain to the macro-fracture. Thecombined analyses of petrofabric, ultrasonic andseismic data has provided a working model foranisotropy, which predicts a decrease in VTI aniso-tropy and an increase in fracture-induced HTIanisotropy with increasing depth towards theporous clean sandstones. As such, the anisotropyserves as a proxy for reservoir quality.

Detailed petrofabric analysis has establishedrules of thumb for the behaviour of each mineralogyobserved in the Clair samples. For example, quartz

and feldspar are consistently aligned and serve as apalaeoflow indicator. In contrast, the calcite israndom in orientation due to its diagenetic growthin pores. The micas align sub-horizontally and arethe most effective minerals in generating CPO ani-sotropy. The predictable nature of the CPO of theseminerals has permitted the development of a

Fig. 10. A quiver plot showing spatial variations in thefracture orientation inferred from AVOA analysis ofthe reflection from the top of Unit V. Strictly speaking,the orientation shows the direction of maximum AVOgradient, which is normally the crack orientation (Hall &Kendall 2003). The length of the ticks is proportional tothe magnitude of the anisotropy can be considered as aproxy for crack density. White regions mark areas wherethe data were not good enough to estimate a reliableAVOA signature.

Fig. 11. (Top) the predicted AVOA anisotropy forreflections from the top of Unit V. The intrinsicanisotropy in Units V and VI with varying superimposedcrack density are included in the calculation. Eachcolumn and row indicates the crack density in Unit Vand Unit VI, respectively; all numbers should bemultiplied by 0.01, thus 2 corresponds to a crack densityof 0.02. AVOA anisotropy magnitudes like thoseobserved in the real data only occur when the crackdensity in the underlying Unit V is much higher than thatin the overlying Unit VI. (Bottom) the range ofanisotropy (AVOA) magnitudes observed in the realdata, inferred from reflections from the interfacebetween Units V and VI.

J.-M. KENDALL ET AL.134

geomathematical model of the intrinsic CPO-induced anisotropy in these polymineral siliciclasticreservoir rocks. We now only need to know thevolume fraction of each mineral and the porosityto predict the anisotropy. This geomathematicalmodel has been validated via comparisons withEBSD predictions, ultrasonic measurements,image analysis (see Maddock 2006). In generalthe CPO anisotropy is appreciable and cannot bedismissed when interpreting anisotropy inseismic data.

Larger-scale anisotropy due to macro-fracturesor periodic thin layering can be superimposed onthe small-scale CPO anisotropy using effectivemedium theories (e.g. Hudson 1981; Backus 1962;Tandon & Weng 1984). This geomathematicalmodel therefore provides a tool for interpreting esti-mates of anisotropy from seismic data (e.g. AVOAor shear-wave splitting). Furthermore, it can be usedto predict anisotropy parameters that can be used indata processing (e.g. anisotropic depth migration).

Comparison with ultrasonic measurements onthese samples has enabled the assessment of theadditional elastic compliances due to intergranulareffects and has allowed the evaluation of the effec-tive anisotropy due to CPO and SPO (cracks,grains) mechanisms. The additional complianceterms are weak in samples with weak anisotropydue to quartz and feldspar. In mica-rich samplesthere is a significant additional compliance due tomicrocracks and intergranular material orientatedparallel to the mica cleavage planes. Such intergra-nular effects further enhance the mica-inducedCPO anisotropy.

Fracture-induced anisotropy at Clair has beenanalysed using AVOA analysis on part of an OBSdataset. The inferred orientations of the fracturesagree with other studies of open fractures in thatpart of the field. A significant advantage of theAVOA analysis over borehole-confined techniquesis that it provides a map of the small-scale spatialvariations in the orientation and magnitude of thefractures and hence holds the potential to helpguide horizontal drilling for optimal well perform-ance. The AVOA analysis is not sensitive to VTIanisotropy, which should decrease towards thebottom of the reservoir. However, it does suggestthat the HTI anisotropy increases towards thebottom of the reservoir. AVOA modelling usingthis geomathematical model confirms that fractur-ing must be increasing across the major reservoirinterfaces.

A workflow has been established for estimatingand interpreting anisotropy in fields where coresamples are available and 3D wide-azimuthseismic data have been acquired. Additionalseismic data would help to constrain the style andcause of anisotropy further (e.g. microseismic data(Teanby et al. 2004) and VSP data (Winterstein

1989)). It remains to be seen whether the rules ofthumb established for the Clair field are applicableto other siliciclastic rocks. This work suggests thatobservations of seismic anisotropy hold muchpotential for deriving valuable information aboutreservoir properties.

We would like to acknowledge ITF and the sponsors of theSAIL project, Hess, BG Group, BP, Chevron,Kerr-McGee, Shell, Total and the Department of Tradeand Industry UK, for funding and constructive input tothe project. We thank the Clair partnership (BP, Conoco-Phillips, Chevron, Shell and Hess. BG Group andKerr-McGee) for releasing the data for publication. Ananisotropic magnetic susceptibility study done by Er. Hail-wood provided helpful constraints on fracturing and paleo-flow indicators in the Clair samples. S. Jolley, R. Knipe,D. Barr and D. Anderson are gratefully acknowledgedfor coordinating this volume. P. Rowbotham andA. Aplin provided constructive reviews that significantlyimproved the readability of the paper.

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