Interpretation of fractures and stress anisotropy in MarcellusShale using multicomponent seismic data
Mehdi E. Far1 and Bob Hardage1
Abstract
Using a data set from the Marcellus Shale, we evaluated the advantages of multicomponent seismic data forfracture and anisotropy studies over conventional P-wave data. Using traveltime and amplitude analysis on pre-and poststack seismic data, we concluded that PS-waves can provide more accurate information about thelocation, orientation, and intensity of natural fractures and stress anisotropy than P-waves. Our analysis indi-cated that regional stress was the main cause of velocity anisotropy. Amplitude variation with offset and azimuthappeared to be more useful for fracture studies, whereas traveltime variations (especially PS-waves) provided abetter indication of regional stress orientations. Principal directions for amplitudes and traveltimes of PP- andPS-waves were different. Misalignment of PP- and PS-waves principal directions suggested that the simplest,most realistic anisotropy model for the fractured Marcellus is monoclinic symmetry.
IntroductionAdvancements in marine and land multicomponent
acquisition and processing have led to several applica-tions for PS-data. These applications include high-resolution, near-surface imaging; fault imaging; seismicimaging of gas reservoirs; lithology estimation usingVP/VS analysis; and direct hydrocarbon indication andseismic anisotropy (Stewart et al., 2003; Hardage et al.,2011).
Hardage et al. (2012) report that multicomponentseismic data are better for exploiting the MarcellusShale than single-component P-wave seismic data, withthe latter (PP data) being the most common seismicdata used across the Appalachian Basin. Specifically,they show that the converted mode (PSV mode) pro-vides better spatial resolution of Marcellus Shale stra-tigraphy than its companion PP mode. The differencein resolution is significant, with PP wavelengths beinglonger than PSV wavelengths by 40% to 50%.
The use of seismic waves to determine the orienta-tion of fractures has received much attention. Lynnet al. (1995) and Lynn (2004a, 2004b) use azimuthalvariations in the reflection amplitude of seismic P-waves to characterize fractured reservoirs. Tsvankinand Grechka (2011) also analyze the AVO and moveoutpatterns in azimuthally anisotropic media. Reflectionamplitudes have advantages over seismic velocities incharacterizing fractured reservoirs because they havehigher vertical resolution and are more sensitive to theproperties of a reservoir (Far et al., 2013b, 2013c).
Evidence from outcrop studies indicates that theMarcellus Shale has at least two sets of near-verticalfracture sets (Figure 1) known as J1 and J2, which canbe either orthogonal or nonorthogonal (Engelder et al.,2009; Engelder, 2011). Therefore, the simplest modelsfor fractured Marcellus Shale can be either orthorhom-bic or monoclinic (with a horizontal symmetry plane,parallel to bedding) media (Far et al., 2013b, 2013c).
Data descriptionThe site selected for this study is in Bradford County,
Pennsylvania. Bradford County lies in the northeastpart of the asymmetric Appalachian foreland basin. Thestudy area traverses Marcellus Shale and Utica Shalegeology as well as numerous brine-filled sandstonesand carbonates that are potential water storage targets.A modern multicomponent 3C 3D seismic survey spansthe site, and an exploratory well was drilled at thecenter point of the 3D seismic image space.
Numerous attributes of seismic data, and particularlyattributes of multicomponent seismic data, are affectedby the source-receiver geometry that is deployed acrossa survey area and the field procedures that are usedto acquire the data. Specifically, an acquisition geom-etry should create adequate stacking folds not onlyfor common-midpoint P-P and S-S data but also forcommon-conversion point P-SV and SV-P data. In addi-tion, a seismic data-acquisition geometry must create afull range of source-to-receiver offsets and azimuths forall P- and S-wave modes. Full-offset and full-azimuth
Manuscript received by the Editor 18 July 2013; revised manuscript received 21 October 2013; published online 25 April 2014. This paper appearsin Interpretation, Vol. 2, No. 2 (May 2014); p. SE105–SE115, 15 FIGS.
Special section: Multicomponent seismic interpretation
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data are particularly important if fracture intervals areto be detected and quantified, or if stress fields andgeomechanical rock properties are to be analyzed(Hardage et al., 2012).
The 3D multicomponent seismic survey that was tobe implemented in this research was intended to bean orthogonal brick pattern in which 13 receiver linesspaced 268-m (880 ft) apart were deployed northwest tosoutheast to form a 3.2 × 3.2 km (2 × 2 mi) square of 3Cgeophone stations, with 97 receiver stations spaced atintervals of 33.5 m (110 ft) along each receiver line. Thetotal number of planned receiver stations was 1261.This receiver grid was to be positioned in the centerof a 8 × 8 km (5 × 5 mi) square array of source stationsarranged in a southwest–northeast brick pattern inwhich 41 source lines were spaced 201 m (660 ft) apart.Each source line consisted of a sequence of four sourcestations spaced at intervals of 67 m (220 ft) with a gap of268 m (880 ft) between successive four-station groups.This source-station pattern created 60 source stationsper line, with a total of 2460 source points across thesurvey area. Each source involved a 1 kg (2.2 lb) explo-sive positioned at a depth of 6 m (20 ft). Figure 2 showssurvey design parameters for the Marcellus 3C 3Ddata set.
The preserved elongate axis of the AppalachianBasin extends southwest–northeast across the west
half of Pennsylvania. The east margin of the basin isoverthrust by the Appalachian Mountains, and the westmargin extends into Ohio and Kentucky. Appalachiansedimentation is controlled by repetition of passive-margin environments, basin deepening, and sedimentstarvation, and advances of immature siliciclastic unitsin a general east–west direction. Much of the recon-struction of deep geology comes from projections oftrends outside of Bradford County compiled by thePennsylvania Geological Survey (Harper, 1990, 2008),the West Virginia Geological Survey (Roen and Walker,1996), and the United States Geological Survey (Miliciand Swezey, 2006).
The stratigraphy and basin structure of northernPennsylvania reflect Precambrian rifting and sedimentdeposition in a passive-margin setting during mostof the Cambrian through Early/Middle Ordovician(Figure 3). Structural features EC, RR, RS, RT, and RW(Figure 3) are elements of the Precambrian RomeTrough. The RT arm of the Rome Trough extendingacross Pennsylvania is offset by several regional faultsand passes in the immediate vicinity of our study site.Late Cambrian events included plate movement of thepresent-day Appalachian area into the evaporative sub-tropical trade-winds belt, where it remained until lateMississippian time (Miall and Blakely, 2009). Figure 4shows lithology sequence and log data from a welldrilled in the middle of the survey area.
Prestack data analysisAzimuthally variant prestack seismic data contain
valuable information about fractures and stress aniso-tropy. As seismic waves propagate through fracturedmedia or a medium under an anisotropic stress state,seismic velocity and amplitude are affected by aniso-tropy. If reflected seismic waves are recorded at thesurface in different azimuthal directions, a systematicvariation can generally be seen on amplitude and
Figure 1. Top: Exposure of Marcellus Shale. This unit isstratified into thin layers and has two orthogonal joint sets,J1 and J2 (Engelder et al., 2009; Engelder, 2011). Bottom: Non-orthogonal J1 and J2 joint sets in Marcellus outcrop on theAppalachian Plateau. J1 joints maintain the same orientationaround oroclinal bends, whereas J2 joints change orientationto remain orthogonal to oroclinal bends.
Figure 2. Map of study area showing location of VSP calibra-tion well relative to planned positions of source and receiverstations used for 3D 3C seismic survey.
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traveltime patterns as a function of azi-muth. Seismic anisotropy becomes moreimportant for PS-waves compared to PP-and SS-waves (Thomsen, 2002). It hasbeen shown that PS-waves are more sen-sitive to fractures than P-waves (Mueller,1991; Beaudoin et al., 1997; Hardage et al.,2011).
Figure 5 shows the variations ofP-wave traveltime and amplitude in acommon-offset section close to the inter-val of interest (top and base Marcellus).The top Marcellus trough is indicated bythe upper arrow on the right and the baseMarcellus by the lower arrow pointing tothe peak. As shown, amplitude and trav-eltimes change in the azimuthal direc-tion. The interpreted fast direction isapproximately 60°. Figure 6 shows thevariations of PS-wave traveltime and am-plitude in a common-offset section (thesame offset as Figure 5), close to the in-terval of interest. The top Marcellus peakis indicated by the upper arrow on theright and the base Marcellus by the lowerarrow pointing to the trough. Again, am-plitude and traveltimes change in theazimuthal direction. The interpreted fast
Figure 3. Study site in Bradford County, Pennsylvania (Harper, 2008).
Figure 4. Well log data (density, gamma ray, and S-wave and P-wave velocities) from a well in the middle of the survey. Syntheticand surface recorded seismic data are also shown. Top Marcellus is the interface between Stafford and the Upper Marcellus. BaseMarcellus is the interface between the Lower Marcellus and Onondaga.
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direction is approximately 80°. However, the azimuthalchange for PS-wave traveltime is much more prominentthan for the P-wave (Figure 5). The time difference be-tween the highs and lows of traveltime for the UpperMarcellus is approximately 40 ms, whereas for P-waves,this difference is only approximately 10 ms. This differ-ence between P- and PS-waves can be seen at all offsets.
Figures 7 and 8 show the changes in amplitudes(Figures 7a, 7c, 8a, and 8c) and traveltimes (Figures 7b,7d, 8b, and 8d) in the Upper and Lower Marcellus for P-and PS-waves, respectively. To facilitate interpretationof amplitude maps, interpreted trends are shown bywhite and black lines. The sinusoidal pattern in PS-traveltimes is clearly detectable, whereas for P-waves,these changes are more subtle. It should be noted that acareful static correction was done on P- and PS-waves
to remove the effect of the near surface. Therefore, azi-muthal anomalies seen in seismic data are assumed tobe due to anisotropy. Another piece of evidence thatproves these variations are not due to near-surface ef-fects is that highs and lows (especially for PS-waves) intraveltimes occur every 90°. Principal directions forP-wave amplitudes and traveltimes are almost thesame, and principal directions for PS-wave amplitudesand traveltimes are also the same. Highs and lows ofamplitudes and traveltimes for P- and PS-waves do notcoincide with one other. This misalignment in principaldirections of P- and S-waves can be attributed to mono-clinic or triclinic symmetry of rocks (Sayers, 1998).Sayers (1998) shows that in order for P- and S-waveprincipal directions to coincide, C36 should be equalto zero. In terms of fracture properties, this misalign-
ment can be caused by the differencesin compliances of fracture sets (Sayers,1998). Sayers (1998) shows that in amedium with multiple sets of verticalfractures, if normal and tangential com-pliances of all fractures are the same,the principal directions of P- and S-waves coincide.
An important issue in fracture model-ing for seismic exploration, which hasnot been sufficiently emphasized in theliterature, is the choice of coordinatesystem. In real-world problems, theorientation of fractures is usually un-known. Therefore, the problem of char-acterizing fractured reservoirs shouldbe looked upon without assuming thatthe orientations are known. In otherwords, one should not assume that thefractures are aligned with the coordinateaxis (seismic acquisition coordinates),nor that the number of fracture sets isknown (Far, 2011). As an example, con-sider one or more than one set of paralleland vertical penny-shaped or asymmetricfractures (either orthogonal or non-orthogonal) in an either isotropic or VTIbackground, where fractures are notaligned with the coordinate system. Forsuch a medium, the stiffness matrix willhave 13 nonzero components in the arbi-trary coordinate system, including C16,C26, and C36 (for more details, see Far,2011). Therefore, even a simple mediumsuch as HTI will appear as a monoclinicmedium in the arbitrary coordinate sys-tem. Therefore, theories developed forfracture modeling must be valid for an ar-bitrary coordinate system and the appro-priate tensor rotation should be appliedto the stiffness matrix (assumed to beinverted from geophysical data) beforeconcluding the type of symmetry. If the
Figure 5. P-wave amplitude and traveltime variation with azimuth at offset ¼8000 ft. Troughs shown by upper arrow show reflections from the UpperMarcellus, and peaks shown by lower arrow show reflections from the LowerMarcellus.
Figure 6. PS-wave amplitude and traveltime variation with azimuth at offset ¼8000 ft. Peaks shown by the upper arrow show reflections from the UpperMarcellus and troughs shown by lower arrow show reflections from the LowerMarcellus.
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Figure 7. P-wave reflection amplitudes (left) and traveltimes (right, in ms) for the top and base of Marcellus. The offset isincreasing from 0 to 14,000 ft.
Figure 8. PS-wave reflection amplitudes (left) and traveltimes (right, in ms) for the top and base of Marcellus. The offset isincreasing from 0 to 14,000 ft.
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medium has orthorhombic or HTI symmetry, the tensorrotation should reveal the zero and dependent com-ponents.
Previous studies on the same data set confirm thatthe fast direction is approximately 80° which is in agree-ment with what PS-wave analysis suggests (e.g., Gaiserand Verm, 2012; Far et al., 2013a). FMI data (Figure 9)also show that orientations of dominant natural frac-tures are almost 80°. However, careful analysis of thecommon-offset section shows that this fast directionmay not be due only to fractures in a specific layer.Figure 10 shows a larger common-offset PS section.This figure shows that the azimuthal variation in trav-eltime (and amplitude) is not restricted only to deeperlayers, but it continues all the way to the surface (ap-proximately 1120 ms at 8000 ft offset). This fact is con-firmed by information from the World Stress Map(Figure 11). Data from the World Stress Map is mainlybased on information from well breakouts and hy-draulic fracturing.
It is assumed that by computing the isochron,propagation effects from above layers are essentiallystripped, similar to layer stripping in PS processing.If traveltime is sensitive enough to the presence of frac-tures, we should be able to see an azimuthal change inan isochron from the top to the bottom of a fracturedlayer. The isochron for the top and base of the fractured
Marcellus (time difference between top and baseMarcellus horizons) is shown in Figure 12a. However,we can only observe subtle sinusoidal variations inFigure 12a at the far offset, which is hard to interpretwith confidence. To facilitate interpretation of the iso-chron map, a traveltime map from the base Marcellus isalso plotted (Figure 12b). What this behavior suggestsis that observed azimuthally variant patterns in travel-times can be caused by a large-scale factor such asregional stress anisotropy, not by fractures. In otherwords, although traveltimes show clearer trends thanamplitudes, they are not sensitive enough to fractures.
Figure 9. Rose diagrams calculated from borehole imagelogs acquired in the central-image calibration well for UpperMarcellus (left) and Lower Marcellus (right).
Figure 10. PS-wave amplitude and traveltime variation withazimuth at offset ¼ 8000 ft in a larger scale. Peaks shown bythe upper arrow (around 1520 ms) show reflections fromUpper Marcellus and troughs shown by the lower arrow(around 1600 ms) show reflections from the Lower Marcellus.
Figure 11. Data from the World Stress Map showing that inthe area studied, orientation of the maximum horizontal stressis approximately 80° from north.
Figure 12. (a) Isochron created in ms as the differencebetween the Upper and Lower Marcellus PS-supergathers.(b) Traveltimes in ms for the Lower Marcellus PS-wave.
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On the other hand, amplitudes could be more reliablefor fracture parameter determination (Far et al., 2013a).
Poststack data analysisThe effect of azimuthal anisotropy can also be seen
on poststack data. P- and PS-waves respond differentlyto fractures and stress anisotropy. Numerous real dataobservations show that PS-waves are more sensitiveto fractures and azimuthal anisotropy than P-waves;therefore, their frequency content decreases more
significantly than does that of P-waves (Mueller, 1991;Beaudoin et al., 1997; Hardage et al., 2011). Also, theamplitudes of the PS-waves are affected more than P-waves. Figure 13a and 13b shows poststack amplitudemaps, along a picked top Marcellus horizon, for P- andPS-waves, respectively. An arbitrary inline is shown (45°
from north). Color bars were adjusted automatically toavoid any bias. As shown, certain areas (possibly chan-nels) are highlighted on the PS-amplitude map that arenot clear on the P-wave amplitude map.
Figure 13. (a) Poststack P-wave amplitudemap. (b) Poststack PS-wave amplitude map.Red colors show low-amplitude areas, whereasblue colors show high-amplitude areas.
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Figure 14a and 14b shows poststack instantaneousfrequency maps (Taner et al., 1979) along the samepicked Marcellus horizon for P- and PS-waves, respec-tively. Interpreting the instantaneous frequency maps,one can also identify geologic features with lower fre-quency content (blue colors). This decrease in frequencycan be attributed to natural fractures, which cause at-tenuation.We also looked at the local frequency attribute
(Fomel, 2007), which shows frequency variation on alocal scale, as opposed to instantaneous attributes thatconsider each sample separately (Figure 15). Figure 15bshows a cleaner image of the low-frequency parts inthe Marcellus Shale, whereas Figure 15a does not give aclear indication of low-frequency sections. The PS-wavelocal frequency map is in a good agreement with thecurvature map that Hardage et al. (2012) study.
Figure 14. (a) Poststack P-wave instantane-ous frequency map in Hz. (b) Poststack PS-wave instantaneous frequency map in Hz.
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ConclusionsOur study shows that for the Marcellus Shale, PS-
waves can provide more accurate information aboutthe existence of natural fractures and seismic azimuthalanisotropy. Variations seen in traveltimes are causedmainly by regional stress rather than fractures. Seismicamplitudes can provide more relevant informationabout fractures than can traveltimes. However, PS-wave
traveltimes indicate the correct direction of maximumhorizontal stress, which is in agreement with all otherevidence, such as stress data and FMI logs. In additionto the correct direction being provided, the degree ofapparent anisotropy in PS-waves is more prominentthan in P-waves. More specifically, for the MarcellusShale horizon, stress anisotropy causes about a 40-msdifference between the traveltimes of fast and slow
Figure 15. (a) Poststack P-wave local fre-quency map in Hz. (b) Poststack PS-wavelocal frequency map in Hz.
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directions, whereas only a 10-ms difference can be seenfor P-waves.
Principal directions of P-wave amplitudes and trav-eltimes are nearly the same, and principal directionsof PS-wave amplitudes and traveltimes are also thesame. However, principal directions of P- and PS-wavesare different. With considering the fact that the geologybeing flat in the study area, and assuming that fracturesare vertical, the simplest, most realistic anisotropymodel for the Marcellus is monoclinic symmetry, witha horizontal mirror symmetry. Complex combination offractures and stress anisotropy could also cause such amisalignment.
Our study shows that poststack PS-wave amplitudeanomalies can be a better indicator of natural fracturesthan P-waves amplitudes. The local frequency attributeprovides a better image of fractured areas than does theinstantaneous frequency attribute.
AcknowledgmentsWe thank the sponsors of the Exploration Geophysics
Lab for their support. The first author also thanks thesponsors of the Texas Consortium for ComputationalSeismology (TCCS) and S. Fomel for their support andcomments. We thank Geokinetics, Geophysical Pursuit,and Chesapeake Energy for permission to use this dataset. Publication authorized by the director of the Bureauof Economic Geology.
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Biographies and photographs of the authors are notavailable.
