detection of potential fractures and small faults using seismic attributes

Often it is difficult to map subtle faults and other trace-to-trace discontinuities hidden in 3D seismic data. They mayappear as minor changes in the seismic waveform that are noteasily correlated using conventional interpretation of seismiccross-sections. To map these changes we have investigated andcomputed several different seismic attributes sensitive to theexistence of small faults and fractures in our target reservoir,the Late Permian Unayzah sandstone of central Saudi Arabia.In addition to the traditional geometric horizon attributessuch as dip and azimuth, we found that both coherence andspectral decomposition were useful in imaging seismic dis-continuities.

In this article we demonstrate the use of seismic attributesfor mapping small faults and fractures in the Unayzah sand-stone—a deep, tight, low-porosity (1-6%) and low-perme-ability (0.1-1.0 mD) clastic reservoir. Our main goal was to gaina better understanding of and predict fault trends and trapsin our study area, where the Unayzah reservoir is gas-bear-ing and occurs at a depth of approximately 4000 m, immedi-ately below the Permian Khuff carbonates. A3D seismic survey(Figure 1) was acquired over the study area in order to definestructural closure, major faults, and any fracture zones thatmight enhance permeability in the Unayzah reservoir. Onlyone well has been drilled in the area, and only 3D seismic datacan provide high-resolution information about faulting andfracturing away from the well control.

Faults were interpreted both conventionally (using cross-sections) and using seismic attributes. Major faults are easilyidentified at the base Khuff reflector due to the large contrastin acoustic impedance between the Khuff carbonates and theUnayzah sand. Smaller faults (throws less than 10 m) or frac-tures in the Unayzah are difficult to map due to low signal-to-noise ratio, the existence of multiples, and the transitionalboundary at the top of the interval. The final structural inter-pretation was improved by use of coherence and spectraldecomposition attributes. Some potential fractured zones weremapped using these attributes.

Fault and fracture mapping. Figure 2 is an E-W seismic lineshowing the interval of interest between the red horizon BKDC(base of the Khuff Formation) and the orange horizon BQSB(base of Qusaiba Formation). The latter is the base of sourcerocks in this field. A large normal fault (red vertical line) hasthrow exceeding 100 m and is easily mapped as cutting thereservoir (between the BQSB and BKDC horizons). Existenceof these conducting faults (which can be interpreted as migra-tion pathways) between these two horizons is vital for hydro-carbon accumulation. However, faults with smaller throwscould only be mapped by computing dip and azimuth attrib-utes from an overlying strong reflector (Jihl Formation), wherea coherent and complex pattern of linear-to-sublinear trendsbecame apparent. A detailed structural analysis of the areashowed the one-to-one relationship between the lineationscomputed from the seismic attributes and the deeper“Hercynian” tectonic structures (Zahrani et al., 2004). Althoughsome larger faults extend into the Permian-CarboniferousUnayzah section, these faults rarely extend shallower throughthe Sudair Formation. To delineate faults with throws of lessthan 20 m or tight flexure zones (associated with intensive frac-turing), seismic attributes should be computed at the reser-

voir level directly. The drawback of this approach is poorerseismic data quality at the Unayzah target. The presence ofnoise and contamination by interbed multiples may also pre-vent successful use of this method within the target reservoir.

Seismic data conditioning. In this paper we define seismicattributes as any measure of the seismic data that helps us bet-ter visualize or quantify features of interest. Until recently, thevast majority of seismic attributes utilized for fracture map-ping were derivative attributes, such as dip magnitude, dipazimuth and curvature. These horizon time-based attributesrely on the relative difference of the time position of adjacentpicks on the seismic event. This implies that to achieve theoptimum results, our seismic data must be filtered to attenu-ate or remove high-frequency random noise prior to horizon

SEPTEMBER 2004 THE LEADING EDGE 903

Detection of potential fractures and small faults using seismicattributesFERNANDO A. NEVES, MOHAMMAD S. ZAHRANI, and STEPHEN W. BREMKAMP, Saudi Aramco, Dhahran, Saudi Arabia

Figure 1. True color Landsat image of Arabian Peninsula (left). The inserton the right shows the study area (small blue box). Oil and gas fields atUnayzah Formation level are represented by green and red colors respec-tively.

Figure 2. E-W seismic line showing interval of interest between the red(BKDC) and orange (BQSB) horizons. The latter is the base of sourcerocks in this area. A large normal fault (red vertical line) can be readilymapped on this section. Existence of conducting faults (migration path-ways) between these two horizons is vital for hydrocarbon accumulation.BKDC is about 2 s in two-way traveltime. Distance between seismictraces is 30 m. Left red curve is sonic log, and five vertical red lines in thecenter of the figure are zero-offset synthetic seismograms.

Downloaded 04 Jun 2011 to 202.115.141.108. Redistribution subject to SEG license or copyright; see Terms of Use at http://segdl.org/

904 THE LEADING EDGE SEPTEMBER 2004

Figure 3. Seismic cross-line before (top) and after (bottom) the application of our principal component filter. Although a substantial amount of randomnoise has been filtered out, the general waveform character has been maintained.

Figure 4. Amplitude spectrum at the reservoir level before (left) and after (right) the application of our principal component filter. Note that the fre-quency bandwidth is nearly preserved. In the color figures, color scale is amplitude in dB, horizontal axis is frequency in Hz and vertical axis is timefrom 0 to 3 s (downwards). In the graphs, vertical axis is amplitude in dB and horizontal axis is frequency in Hz.


autopicking. This data conditioning is typically done by appli-cation of spatial filters, such as median, f-xy or f-k on the post-stack seismic data volume. These filters tend to suffer frommixing artifacts and are prone to have a “wormy” appearance.In this study we adopt an eigenimage-based poststack filteroperating in the time-slice domain (Al-Bannagi et al., 2004).This principal component filter offers the advantage of atten-uating both acquisition footprint and random noise in oneprocess. Moreover, it preserves the relative amplitude of the

data accurately and has a negligible effect on frequency con-tent. It also performs well in the presence of dipping reflec-tors while preserving seismic data character.

Figure 3 shows a seismic crossline before (top) and after(bottom) the application of the principal component filter. Itcan be seen that a significant amount of random noise has beenremoved while maintaining the general waveform character.The application of this filter produced a noticeable increasein the signal-to-noise ratio, which is a prerequisite for reliableautopicking and subsequent attribute extraction. Autopickingconsistency is necessary for the identification of subtleattribute-defined faults (Lawrence, 1998). Figure 4 shows thefrequency amplitude spectrum of the data in Figure 2. Theupper panel is the spectrum before the application of the fil-ter and the lower panel is the spectrum after application. Thisillustrates that the frequency bandwidth has been nearly pre-served. Figure 5 shows the impact of the application of the fil-

SEPTEMBER 2004 THE LEADING EDGE 905

Figure 5. Spectral decomposition slice at 32 Hz at the reservoir levelcomputed before (left) and after (right) the application of our principalcomponent filter. The application of the filter has helped in obtaining asignificantly sharper seismic frequency image, specifically at the area ofinterest (west of the well). Location of the well is indicated by the bluesolid circle. Area of the figure is about 18 � 15 km.

Figure 6. A seismic crossline showing BKDC top reservoir (yellow hori-zon just below 2 s). The horizon below (blue color) is BQSA base of sourcerocks in this area.

Figure 7. (a) Time-structure of top reservoir (color scale is two-way trav-eltime in ms). This time map indicates the existence of faults boundingthe large red areas. The black solid line shows the location of the E-Wseismic line displayed in Figure 5. Blue solid circle shows the location ofthe well on a structural high. Area of interest is west of the well. (b)Amplitude map at top reservoir (color scale is maximum seismic ampli-tude). Note that there is no clear indication of fracturing that could beextracted confidently from these two traditional attribute maps.

Figure 8. Dip (left) and azimuth (right) map calculated from the timevalues of Figure 7a. Note that the full extent of the east flank of the faultblock (circled in yellow) is better delineated by the azimuth attribute,whereas an indication of a possible fractured zone (highlighted by the redcircle) is better inferred from the dip attribute. Color scales for dip (1 to 7)and azimuth (0-360) are both in degrees. Area of the figure is about 18 �15 km.

Figure 9. (a) Coherence (left) and spectral decomposition (right) maps atthe reservoir interval. The red dashed lines suggest the existence of enechelon faults cutting the west flank of the faulted block. The 26-Hz spec-tral slice gives additional and independent information than coherence.The red and green circles point out areas where the frequency map showeda clearer image than the coherence map. The horizontal blue line is thelocation of the seismic line shown in Figure 9b. Area of the figure is about18 � 15 km. (b) Seismic crossline showing the existence of several faults.The dashed red circle indicates the interval where faults were bettermapped using coherence attribute, while the red solid circle shows faultswhich were better mapped by spectral decomposition attribute. Verticalaxis is two-way travel-time. BKDC horizon is indicated by the greenarrow. East is to the right of the figure.


ter on the computation of spectral decomposition slices.Spectral decomposition is a technique that uses discrete Fouriertransforms (Partyka et al., 1999) to decompose the recordedseismic wavefield into discrete frequency slices (typically atevery 1-2 Hz). Figure 5 shows the 32-Hz slice at the reservoirinterval computed before (left) and after (right) the applica-tion of principal component filtering. The application of thisfiltering has resulted in a clearer seismic frequency image,specifically in the area of interest (west of the well).

Seismic attributes. Figure 6 shows a crossline extracted fromthe 3D survey. Note that seismic data quality is reasonablygood (medium signal-to-noise ratio), so a confident interpre-tation can be made. It is difficult to see any indication of frac-turing at the reservoir level on this seismic line. The data wereinterpreted on a 10 � 10 inline/crossline grid, and thenautopicked over the entire seismic volume. The interpretedhorizon (BKDC), just below 2 s, is the seismic marker at thebase of the Permian carbonate section. The target reservoir isimmediately below this regional marker. The BKDC time andseismic amplitude maps are shown in Figure 7; no clear indi-cation of minor faulting or fracturing is apparent on these twomaps. Figure 8 shows the dip and azimuth map computedfrom Figure 7a. The dip and azimuth attributes are the mag-nitude and the direction of the time gradient vector computedat each trace of the picked horizon. Dip and azimuth shouldbe displayed and analyzed separately, because faults and frac-tures may be noticeable on one, but not the other (Rijks andJauffred, 1991). Afault is typically well defined on the azimuthmap when the dip direction of the fault plane is opposite tothe dip direction of the layers. Conversely, a fault will beexpressed on the dip map when the dip angle of the fault planeis distinctly different from the horizon dip. Both maps clearlyhighlight the main fault block. In the dip map, the colorswhite to light gray show clearly the main fault block, and theexistence of fractured zones (highlighted in red) can be eas-ily inferred. The whole extent of the east flank of the fault blockis better delineated by the azimuth attribute.

Although these maps adequately define the general struc-tural framework of the prospect, maps generated from non-horizon-based attributes, such as coherence and spectraldecomposition, reveal even more information. Coherenceattributes recognize and enhance trace-to-trace discontinu-ities in the data. Acontinuous seismic event shows large coher-ence values, while a broken horizon, due to faulting orfracturing, exhibits smaller coherence values. One main advan-tage of using coherence attributes is that they do not requireprior seismic interpretation. The coherence volume was com-puted using a dip-steering algorithm which helped to removethe effects of dip.

The coherence map at the reservoir level is shown in Figure9a (left). The light regions, characterized by high coherencevalues, are interpreted as unfractured areas. Higher values ofcoherence correspond to high conformity of the surroundingseismic traces, and low similarity is represented by the darkercolors. This coherence map enabled us to map a fracture pat-tern that was not visible on conventional seismic displays. Notethe SW-NE striking lineaments (red dashed lines) on the westflank of the fault block. The existence of these en echelonfaults, showing a right lateral strike-slip component, mightindicate lack of seal integrity.

Further information can be obtained by computing andgenerating spectral decomposition maps. To date, most pub-lications have shown the application of spectral decomposi-tion for stratigraphic analysis of clastic channel systems. Inour study area, spectral decomposition has been used to delin-eate faults and fracture zones. The right side of Figure 9a is a26-Hz spectral slice that shows additional and independentinformation not seen using coherence. The red and green cir-cles illustrate areas where the frequency map has a distinctand clearer image than the coherence map. Both attributes weregenerated over the full seismic volume, so that deeper hori-zon slices and arbitrary time slices could be interpreted fordefining the structural growth history. Figure 9b shows a seis-mic crossline across several faults in Figure 9a. Note that thefaults on the west side of the structure (dashed red circle inFigure 9b) were better mapped on the coherence volume,while the faults on the eastern side (solid red circle in Figure9b) were better mapped using the spectral decompositionslice.

Interpretation and conclusions. The traditional horizon-basedinterpretation approach, combining autopicking followed byattribute extraction (dip and azimuth), was refined by usingnongeometric attributes such as coherence and spectral decom-position. Seismic data conditioning, through the applicationof an eigenimage-based filter, was able to attenuate high-fre-quency components (random noise), while preserving rela-tive amplitude, frequency bandwidth, and the generalwaveform character. This was critical to achieving reliableautopicking results throughout the entire survey in a phase-consistent manner. The detailed interpretive quality wasimproved by the use of coherence and spectral decomposi-tion attributes in our study, and several potential fracturedzones have been mapped. Each attribute provided additionaland independent information about the fracture system thatcould not be inferred from one attribute alone. Better map-ping of faults and fractures provided insight into the locationof possible closures, vertical hydrocarbon migration conduits,and regions of fracture-permeability-enhanced reservoirs.Faulting and fracturing in our study were extensive and arebelieved to have a critical effect on fluid flow in the targetedUnayzah reservoirs.

Suggested reading. “Eigenimage footprint removal” by Al-Bannagi et al. (6th Middle East Conference, Bahrain, 2004).“Seismic attributes in the characterization of small-scale reservoirfaults in Abqaiq Field” by Lawrence (TLE, 1998). “Interpretationalapplications of spectral decomposition in reservoir characteriza-tion” by Partyka et al. (TLE, 1999). “Attribute extraction: Animportant application in any detailed 3D interpretation study”by Rijks and Jauffred (TLE, 1991). “Dip/azimuth attribute as atool to map structures” by Zahrani et al. (6th Middle EastConference, Bahrain, 2004). TLE

Acknowledgments: We thank Saudi Aramco for permission to publish thispaper. Special thanks to Mohammad Al-Bannagi for his work on the prin-cipal component filter algorithm, and to Greg Douglass and Abdulla Al-Yahya for reviewing the manuscript and helping with several interpretiveprocessing tasks related to this project.

Corresponding author: [email protected]

906 THE LEADING EDGE SEPTEMBER 2004


detection of potential fractures and small faults using seismic attributes

Documents