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8/12/2019 Application of Volumetric Seismic Discontinuity Attribute for Fault Detection Case Study Using Deep-water Niger De

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M a r i n e a n d o f f s h o r e t e c h n o l o g y

424 The Leading Edge April 2013

SPECIAL SECTION: M a r i n e a n d o f f s h o r e t e c h n o l o g y

Application of volumetric seismic discontinuity attribute for fault

detection: Case study using deep-water Niger Delta 3D seismic data

echniques for detecting faults have been applied to a 3Dseismic volume acquired in the outer fold and thrust beltin the deep-water Niger Delta. Firstly, the dip and azimuth ofseismic traces in the data were calculated in a volume referredto as the raw steering data. Te data were further improvedby calculating two additional generations of dip volumesrepresenting localized and subregional structural dips referredto as the detailed and background steering volumes,respectively. A multitrace similarity attribute volume was thencalculated with the reflectivity and background dip-steeringdata as the input. Te attribute data detected discrete zonesof dip and similarity anomalies, trending WNW-ESE, that

represented the location of discontinuities in the area. Teanomalies may not have been seen clearly in the reflectivityand similarity data calculated without the application ofdip-steering.

Te workflow demonstrates the usefulness of applyingdip-steering algorithms for fault detection and in assessingthe structural framework of large 3D seismic data prior todetailed interpretation.

BABANGIDAW. JIBRIN, TIMJ. RESTON, andGRAHAMK. WESTBROOK, University of Birmingham

IntroductionOne of the most daunting tasks in the structural interpreta-tion of seismic data is delineating seismic anomalies related tofaulting from noise, both of which may co-exist. Seismic at-tributes have been used for many years to delineate faults andstratigraphic features that are difficult to map using standardamplitude seismic data. Te coherence cube (Bahorich andFarmer, 1995) has traditionally been used to highlight discon-tinuities along horizons tracked on seismic data. However, arecent significant development in seismic attribute processingis the concept of 3D volume extraction of attributes fromseismic data guided by structural dips. In this example, we

apply volume seismic discontinuity attribute extraction tech-niques to seismic data that image parts of the outer fold andthrust belt in the deep-water Niger Delta using algorithmsdeveloped by ingdahl (2003). Perspective volume viewsand time slices extracted from the structurally enhanced dataare used to illustrate results obtained from the extraction ofthe volume seismic discontinuity attribute. Faults play a keyrole in oil and gas exploration and production and as thesearch for hydrocarbon moves to geologically complex fron-tier deep-water settings, the need for accurate detection andmapping of faults for subsurface structural modeling becomesimperative. In addition, volume discontinuity seismic attri-butes can potentially be used to predict the seismic structure

of fault zones ahead of drilling expensive oil and gas wells.

Methods

Te 3D data are a subset of a 3000-km2 volume acquiredby Petroleum Geo-Services (PGS) in water depths rangingfrom ~1300 m to ~2700 m (Figure 1). Te data image partsof deep-water Niger Delta compressional domain describedas the outer fold and thrust belt (Corredor et al., 2005). Tedata have inline and crossline spacing of 25 and 12.5 m, re-spectively. Te recording interval is 9 s, with a sampling rateof 4 ms. Spectral analysis of the data volume shows that thedominant frequency varies with depth and ranges from 40

Hz to 60 Hz in the in-terval where most dis-continuities are located(36 s two-way travel-time). A frequency of46 Hz was used to cal-culate the vertical reso-lution of the data; thisfrequency appears to bethe strongest in the am-plitude spectrum plot.Te vertical resolution

varies from ~10 m in

Figure 1. opographic map of the Gulf of Guinea showing the study area.

Attribute Time gate (ms) Step-out Dip-steering Statistical operator

Raw steering - (1,1,1) - -

Detailed steering - (0,0,5) - -

Background steering - (5,5,0) - -

Standard similarity (24,24) - No steering Minimum

Dip-steered similarity (24,24) (1,1,1) Full steering Minimum

Table 1. Seismic attributes parameter settings.


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It is mathematically the Euclidean distance in hyperspacebetween vectors of the segments, normalized between 0 and1 to the sum of the lengths of the vectors (Equation 1).

A high similarity (maximum of 1) means the trace seg-ments are similar in waveform and amplitude. If the twotraces show a lot of dissimilarity (minimum of 0), the sim-ilarity is interpreted to be low and may be due to locallydisplaced/disrupted strata usually at the location of faults.Similarity is thus a seismic discontinuity attribute, as shown

in Equation 1.

shallower sections but decreases to ~18 m in deeper sectionsof the data based on the downward increase in interval veloc-

ity. Te horizontal resolution is ~100 m based on the widthof the Fresnel zone.Dip-steering.Te first stage of the workflow (Figure 2) is

the 3D extraction of the dip and azimuth of the seismic trac-es. In extracting the data, attributes are conceptually guidedalong a 3D surface on which the seismic phase is approxi-mately the same, thus creating a virtual horizon at each posi-tion along the dip/azimuth from trace-to-trace (Figure 3a). Aseismic event is followed from the central position by track-ing the position of the local dip and azimuth in the data.race segments are aligned horizontally without the applica-tion of dip-steering (Figure 3b); however, the application offull steering ensures the location and azimuth of the traces isupdated at every trace location, thereby enhancing the con-trast and resolution of multitrace seismic attributes in thepresence of structural dips (Figure 3c).

Te first dip-steering data were calculated using the BGFast Steering filter and referred to as raw steering volume.Te filter is based on the analysis of the vertical and hori-zontal gradient of amplitude data to calculate estimates ofreflector dips.

From the raw steering data, two structurally enhanced vol-umes were calculated by applying structure-oriented filtersand referred to as detailed and background steering vol-umes. Te detailed steering volume contains the localized dip

of the seismic traces, while the background dip-steering vol-ume was calculated by applying a lateral filter to the detailedsteering data (i.e., dip is averaged). Te steering data werethen batch processed and stored in 3D volumes. Detailed de-scription of the mathematics of dip and azimuth processingapplied to the seismic data used in this study is discussed iningdahl (2003), ingdahl and de Groot (2003), ingdahland de Rooij (2005). Similarity attribute.Te concept of similarity applied tofault detection in seismic data was developed by ingdahl(2003). Similarity (S) is calculated by measuring wave-form similarity of adjacent trace pairs and the time differ-

ence between the traces interpreted as vectors (Figure 4a).

Figure 2. Workflow for volume fault detection techniques applied tothe seismic data.

Figure 3.3D schematic illustration of the concept of dip-steering.Te arrows indicate the steering directions (a). (b) and (c) are a 2Dschematic illustration of dip-steering. In (b) no steering is applied tothe data and the trace segments are aligned horizontally. However, in(c) the application of full steering correction ensures the location andazimuth of the traces are updated at every trace location.

Figure 4. Schematic illustration of the similarity between two tracesegments and the effect of dip on trace similarity computation. Tesimilarity between the two trace pairs is mathematically the Euclideandistance between vectors of the segments normalized to the sumof the lengths of the vectors (a). In (b) trace segments A and B aredifferent when compared horizontally. A has high values when B haslow; however, if the dip is considered, trace B is shifted downwards milliseconds before the comparison and the two segments will be

similar, thereby ensuring minimal effects of dipping reflectors onsimilarity calculations.


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S= 1 |v| + |u|

|trace segment 1 trace segment 2||trace segment 1 + trace segment 2|

i.e. (1)

f (t1,x

v,y

v) f (t

1,x

u,y

u)

f (t1+ d

t,x

v,y

v) f (t

1+ d

t,x

u,y

u)

v= . , u= .

. . f (t2+ d

t,x

v,y

v) f (t

2+ d

t,x

u,y

u)

f (t2,x

v,y

v) f (t

2+ d

t,x

u,y

u)

tis the time-depth of investigation, dtis the sampling interval,

t1and t

2are the limits of the time gate, (x

v,y

v) and (x

u,y

u) are

the two trace positions that are compared andfis the ampli-tude value. Te similarity attribute was calculated using user-definedparameters based on the quality, frequency, sampling rate,

and bin size. Other factors include the desired wavelengthof structures to be detected, size of the data and comput-ing hardware capabilities. Te time-gate operator determinesthe desired wavelength of structures to be detected. For this

study, a time gate of +24 ms and 24ms, equivalent to the average seis-mic wavelength within the windowof investigation, was used to calcu-late the similarity of seismic tracesin the data. Te step-out defines theradius of investigation (in the inline,crossline, and sample format) anddetermines the sampling size. A step-out of 1,1,1 implies that the samplingwas along every inline and crossline.

Similarity is sensitive to amplitudedifferences between trace segmentsin addition to wave shape. Te dif-ferences in the response of attributesat fault locations depend on the dipof the traces such that backgroundsimilarity will be low while the con-trast between discontinuities and thebackground will be high. Te steer-ing algorithm determines the direc-

tivity of the attribute extraction suchthat the application of full steeringmode ensures that the attributes arecalculated from trace-to-trace alongstructural dips.

Te trace segments used are shift-ed upward or downward so that theyhave the same phase as the centralposition of investigation. Te posi-tion returned from the analysis isdetermined by the output statisticaloperator because the operation in-

volves the comparison of more thanone trace segment. Te position ofthe minimum similarity was selectedas the output statistical operator forthe similarity attribute calculation inthis article. For fault detection, the appli-cation of dip-steering reduces thesensitivity of similarity to dippingreflectors (that may not be due todiscontinuities) by aligning adja-cent trace segments with a lag time.Te result is that background noise

is attenuated and the detectability

Figure 5. Perspective views of seismic amplitude volume (a), raw steering volume (b), detailed

steering volume (c), and background steering volume (d) from 3 to 6 s two-way traveltime. Notethe enhanced imaging of discrete zones of dip anomalies in the detailed and background steeringdata indicated by the red arrows in (c) and (d). Te red outline indicates the location of time slicesextracted from the data at 4 s two-way traveltime and shown in Figure 5. Vertical exaggeration is ~3x.

Figure 6. ime slices sampled at 4 s two-way traveltime through the seismic amplitude volume(a), raw steering volume (b), detailed steering volume (c), and background steering volume (d). Tered arrows show zones with extreme negative dip values in a predominantly WNW-ESE orientation(highlighted in the dip-steering data). Extreme positive dip values (green arrows) represent structuralhighs (regions of folding) related to thrusting. Te red arrows show the locations of discontinuities.

Where,


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Figure 7. Seismic amplitude volume (a) and similarity volume (b) from 3 to 6.0 s two-waytime. Te red and green arrows highlight good correlation between high similarity and strongreflectivity and low similarity and a weak pattern of reflectivity, respectively. Te red outlineindicates the locations of time slices extracted at 4 s from the data and shown in (c) and (d).Vertical exaggeration is ~3. Note the well defined pattern of discontinuities trending WNW-ESE(red arrows) and the EW-trending zone of intense disruption (green arrows), clearly seen on thesimilarity time slice.

Figure 8. Similarity attribute volume calculated without dip-steering (a) and with dip-steering(b) from 3 to 6 s two-way traveltime. Vertical exaggeration is ~3x after two-way traveltime. Tered outline indicates the locations of time slices extracted at 4 s from the nonsteered (c) and dip-steered similarity attribute volumes (d). Compared to nonsteered similarity data, the dip-steeredsimilarity attribute has enhanced the sharpness of the WNW-ESE trending zones of low-similarity,structures related to fault interaction (red arrows) and the arcuate zone of intense disruption (greenarrows).

of discontinuities is enhanced dueto the dissimilarity of the trace seg-ments (Figure 4b). Te similarityattribute volume presented in thisarticle was calculated using back-ground steering and seismic reflec-

tion data as the input.Previous work has shown thatsimilarity calculated with steeringdata representative of a regional dip(background steering data) providesthe best similarity of seismic traces(Brouwer, 2007; Brouwer and Huck,2011). Once the parameters are test-ed and optimal values selected, theattributes are extracted on-the-flyand evaluated prior to multi-tracevolume batch processing. Te datacan then be exported for interpreta-

tion in SEG-Y compatible formats.able 1 summarizes the seismic at-tribute extraction parameter setting.

ResultsFigure 5 shows the input seismic re-flection volume and the three genera-tions of dip-steering data spanning36 s two-way traveltime. Te redarrows in Figure 5b, Figure 5c, andFigure 5d show discrete zones ofdip anomalies difficult to see in the

seismic reflection data in Figure 5a.Figure 6 shows time slices extractedat 4 s from the seismic reflectivityand steering volumes, respectively.Te detailed and background steer-ing data show a clear pattern of zonesof dip anomalies, trending WNW-ESE, that are interpreted to representthe location of major discontinuitiesin the area.

Extreme values of dip (dark andlight shades) represent zones of high

dipping events and discontinuities(red arrows) are detected at the loca-tion of extreme negative dips whilethe light shades represent zones ofextreme positive structural dips re-lated to folding adjacent to the dis-continuities (green arrows). Figure 7 is a volumetric com-parison between seismic reflectivity(Figure 7a) and similarity attributevolumes (Figure 7b) spanning 36 stwo-way traveltime. Comparison ofthe two data sets shows good correla-

tion between the pattern of seismic


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reflectivity and similarity. Te red arrows show a region ofhigh similarity that matches a strong pattern of reflectivity. Tegreen arrow shows an arcuate zone of low similarity and weakreflectivity in the two volumes. In Figure 7c and Figure 7d, wecompare seismic amplitude and similarity time slices extractedfrom the data at 4 s. Te similarity attribute data show the edgesof the large discontinuities with high contrast and also highlight

structures that may be due to fault growth and interaction (redarrows). Te interpretability of the arcuate zone of complex dis-ruption characterized by low similarity (green arrows) has alsobeen improved. In Figure 8, we demonstrate the usefulness of applyingdip-steering for similarity calculation. In Figure 8a, similaritywas calculated without the application of dip-steering; in Figure8b, dip-steering was applied to calculate the similarity attributedata. ime slices extracted at 4 s from the two data sets showthat discontinuities in the dip-steered data can easily be recog-nized with improved imaging of the complex zone of disruptionindicated by the green arrows (Figure 8c and Figure 8d).

Te sharpness of the edges of the discontinuities has also beenimproved significantly (red arrows). In contrast, the nonsteeredsimilarity attribute has a lower contrast at the location of thezone of discontinuities trending WNW-ESE.

ConclusionWe have presented a workflow for volumetric detection of faultsin the deep-water Niger Delta using dip-steering and similari-ty attribute data. Perspective 3D views and time slices extractedfrom the data show how subtle structural details that may notbe clearly seen or missed in standard similarity and seismic re-flectivity data or when tracked along a horizon can be detectedin the improved data. Te structurally enhanced data detecteddiscrete zones of dip anomalies and discontinuities representing

the location of thrust faults in the outer fold and thrust belt inthe deep-water Niger Delta.

ReferencesBahorich, M. and S. Farmer, 1995, 3-D seismic coherency for faults and

stratigraphic features: Te Leading Edge, 14, no. 10, 10531058.http://dx.doi.org/10.1190/1.1437077

Brouwer, F., 1997, Creating a good steering cube: Opendtect.Brouwer, F. and A. Huck, 2011, An integrated workflow to optimize dis-

continuity attributes for imaging of faults: Presented at 31st AnnualConference of GCSSEPM, Attributes: New views on seismic imag-ingTeir use in exploration and production.

Chopra, S. and K. Marfurt, 2011, Coherence and curvature attributes onpreconditioned seismic data: Te Leading Edge, 30, no. 4, 386393,http://dx.doi.org/10.1190/1.3575281.

ingdahl, K., 2003, Improving seismic chimney detection using direc-tional attributes, in M. Nikravesh, L. Zadeh, and F. Aminzadeh, eds,Developments in Petroleum sciences: Elsevier.

ingdahl, K. and P. de Groot, 2003, Post-stack dip and azimuth process-ing: Journal of Seismic Exploration, 12, 113126.

ingdahl, K. and M. de Rooij, 2005, Semi-automatic detection of faultsin 3-D seismic data: Geophysical Prospecting, 53, no. 4, 533542,http://dx.doi.org/10.1111/j.1365-2478.2005.00489.x.

Acknowledgments: Te authors thank PGS (Exploration) for providingthe 3D seismic data and permission to use the data for this study. We thankdGBE Earth Sciences for donating Opendect software for academic use atthe University of Birmingham. Saleh Al-Dossary (Saudi Aramco) and Ar-naud Huck (dGB Earth Sciences) are thanked for reviewing early draftsof the manuscript and for providing useful suggestions that improvedthe quality of the work. Te study is part of Jibrins doctoral researchat the University of Birmingham sponsored by the Nigerian Petroleumechnology Development Fund (PDF).

Corresponding author: [email protected]

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