Interbed Multiple Attenuation

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M u l t i p l e a t t e n u a t i o n

936 The Leading Edge August 2011

SPECIAL SECTION: M u l t i p l e a t t e n u a t i o n

Applications of interbed multiple attenuation

Surface-related multiple attenuation schemes have provenuseful in attenuating a significant proportion of the high-

amplitude multiple content in seismic data. Although generallysufficient for exploration purposes, there is growing recognitionthat more complicated interbed multiples exist within the dataand that they may interfere with the interpretation of reservoirsand other areas of interest. In this paper, we examine a data-driven method and a model-driven method for attenuatinginternal multiples showing both synthetic and real-worldexamples of their application.

Introduction

Interbed multiple contamination has been observed previouslyduring reservoir studies from the North Sea, Middle East, and

Asia Pacific regions, where shallow carbonates or volcanics gen-

erate strong reverberations. Frustrating to all parties, this noisehas, for a long time, been categorized as "too difficult" to ad-dress because it resists traditional multiple attenuation meth-ods; the multiples and primaries have similar moveout reducingthe effectiveness of Radon-based methods, the multiples andprimaries have similar frequencies and amplitudes reducing theeffectiveness of predictive filtering, and the multiples and pri-maries have similar dips reducing the effectiveness of f-k filters,tau- p demultiple, and migration methods implementing dip-discrimination techniques during the imaging (for example,controlled-beam migration).

Te discovery of the upi/Lula Field, offshore Brazil, in

2006, and later discoveries of Jupiter, Sugarloaf, Iara, Azulao,and Iracema have confirmed the potential for significant, presalt,oil accumulations in the Santos Basin. One of the seismic imag-ing challenges in the basin, however, is the existence of high-am-plitude interbed multiple contamination across the reservoir. Anexample can be seen in Figure 1 which shows a migrated image.

Figure 2 shows near-offset data from a second line in theSantos Basin. Note that the first surface-related multiple appears

well below the presalt target. Strong interbed multiples, how-ever, contaminate the presalt primaries. Te interbed multiplesare generated by a series of strong reflectors above the target.Te water bottom, top of Albian layer, top of salt, and layeredevaporites can all contribute toward generating strong interbedmultiples. Te strongest of these multiples appear below syn-cline structures in the top of salt and layered evaporites, due to afocusing effect that traps multiple energy in the minibasin (Picaand Delmas, 2008). Te migrated results, Figures 1 and 2b,show the interbed multiples cross-cutting the reservoir. Teseinterfere with the interpretation of the base of salt event, thereservoir, and the presalt faulting.

Following the advent of data-driven surface-related multipleattenuation by Berkhout et al. (Berkhout and de Graaff, 1982;Verschuur, 1991; Berkhout and Verschuur, 1998), many authorshave extended the data-driven concept to include interbed mul-tiple attenuation. Many methods based on the work of Jakubo-

wicz (1998) have appeared in the literature, for example Ikelle

M ALCOLM G RIFFITHS , J ESHURUN H EMBD , and H ERVÉ P RIGENT , CGGVeritas

(2004). Tese all require a two-trace convolution followed by asingle-trace correlation or some combination thereof. Equivalentmodel-driven methods also exist based on the same concept, no-tably from Pica and Delmas (2008). Te concept has also beenconverted to the inverse data space by Luo et al., (2007). Teimplicit limitation of this method is that it predicts only inter-nal multiples associated with a single horizon, typically definedinside a single layer, predicting internal multiples from the sur-rounding reflectors that will cross this horizon along their path.

A second extension to the data-driven internal multiple at-tenuation methods, again pioneered by Berkhout et al., (Berk-hout and Verschuur, 1997 and 1999; Berkhout, 1999), utilizescommon focal point transforms (CFP) to partially redatum(receiver side) and fully redatum the input seismic to the levelof the multiple-generating horizon. Te method is similar to

Jakubowicz’s method in that it requires a two-trace convolu-tion followed by a single-trace correlation and predicts only themultiples associated with a single horizon or layer, but it alsorequires two redatuming steps and the associated CFP operators.Kelamis et al. (2002) and Ala’i et al. (2006) demonstrated thismethod on land data highlighting the difficulty associated withthe estimation of the CFP operators.

Yet another method of internal multiple attenuation hasbeen pioneered by Weglein et al. (1997) using the inverse-scat-tering series. Tis method is again fully data-driven but has theadded benefit of being capable of predicting all interbed mul-tiples in a single iteration, (Weglein et al., 2008). Depending

upon the implementation complexity, it is possible to predicteither an approximation to the internal multiples (leading term),or through an infinite series, the exact internal multiple contri-bution, (Ramirez, 2008). Tis method has been previously dem-onstrated on marine data by Otnes et al. (2004) and on landdata by Fu et al. (2010).

With the exception of these inverse-scattering infinite series,all these methods rely upon some form of adaptive subtractionin order to attenuate the internal multiple. Tis has a plethoraof problems associated with it, foremost of which is potentialprimary damage. None of these issues are covered in this pa-per—see Abma et al. (2002) for a detailed analysis of adaptivesubtraction techniques.

Tis paper describes the application of two of the above

Figure 1. Migrated image showing strong interbed multiples in data

from the Santos Basin. (Courtesy of Petrobras)


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methods for attenuating internal multiples: a 3D data-drivenmethod derived from Jakubowicz, and the 3D model-drivenmethod from Pica. Te focus is on the interbed multiple con-tamination at the reservoir level for various exploration inter-ests within the Santos Basin, Brazil. Te results, however, are

equally applicable to reservoir-level contamination observed inthe North Sea, Middle East, and Asia Pacific.

Methodology

Many factors influence the effectiveness of the various internalmultiple attenuation algorithms; geological complexity (mul-tiple periodicity) and acquisition design (offset coverage andspatial sampling) are two key factors. It is then not surprisingthat the application of any algorithm should first consider theinput limitations.

Te 3D data-driven method, following Jakubowicz, is il-lustrated in Figure 3. An arbitrary horizon is chosen at a pointdeeper than the known multiple-generating horizon. Te inter-

nal multiples predicted are then limited using various mutingmethods but are always those having the first upward reflectionbelow the user-defined horizon, a downward reflection abovethe user-defined horizon, and a final upward reflection belowthe user-defined horizon. Te prediction is given mathemati-cally in Equation 1.

P 212

= P 2 P

1*P

3(1)

P1 refers to the wavefields from the surface to the multi-

ple-generating horizons, P2 refers to the source-side wavefield

reflecting from below the multiple-generating horizon, and P3

Figure 2. Interbed multiples in data from the Santos Basin. Note thestrong interbed multiples obscuring the presalt target in the near-offset data (left). e first surface-related multiple appears at thebottom of the figure, below the target. e Kirchhoff-migrated image(right) shows that migration swings crossing the base of the target andinterfering with fault interpretation at the target. (From Hembd et al., 2011)

Figure 3. Prediction of multiples reflecting downward from horizon1.

Figure 4. Backward extrapolation of the muted input data. (FromPica and Delmas 2008)

Figure 5. Previously stored wavefield will illuminate the reflectivity

section. (From Pica and Delmas, 2008)

Figure 6. Downward extrapolation of the wavefield resulting fromthe secondary sources created at stage 2. (From Pica and Delmas, 2008)

Figure 7. Final upward extrapolation. (From Pica and Delmas, 2008)


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refers to the receiver-side wavefield reflecting from below themultiple-generating horizon. P

1* refers to the complex conju-

gate of P1. Equation 1 neglects both the source term and the

surface reflectivity which can both be compensated for using aleast-squares matching filter during the subtraction stage. It alsoincludes both primary and higher-order multiple contributionsto the interbed multiple prediction. Te task of localized waveletmatching falls upon the subtraction.

Without knowledge of the downward reflection point, how-ever, it becomes necessary to sum Equation 1 over a user-definedaperture. Te method places a high reliance upon the input ac-quisition density. For accuracy, it requires shot and receiver sta-tions at all possible surface locations as well as measurements

of the wavefield at zero offset. Not surprisingly, these are the

same limitations found in the surface multiple implementationand, as such, the method is found to work reasonably well indeeper water where the near offsets can be easily extrapolatedand in wide-azimuth acquisitions where the azimuthal samplingis closer to ideal.

Te 3D model-driven method also follows the logic of Jakubowicz. In it, the input data are backward-propagatedthough a predefined model to the user-defined arbitrary horizon(Figure 4). Te horizon is defined in the same way as in thedata-driven method. Prior to the back-propagation, the inputdata must first be muted to remove all reflections above the tar-get horizon. Once at the target horizon, the wavefield is upwardextrapolated through the model to illuminate the shallow events,

thus identifying the downward reflection points in Figure 5.

Figure 9. RTM comparison of the Santos Basin synthetic data: (left) before and (right) after data-driven interbed multiple attenuation. (FromHembd et al., 2011)

Figure 8. 2D synthetic data of the Santos Basin used for the data-driven interbed multiple attenuation: (left) input, (center) subtraction result,and (right) difference. (From Hembd et al., 2011)


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For each possible reflection point, the wavefield is downwardpropagated through the model a second time until it reaches thereflecting horizons (Figure 6) where it is finally forward propa-gated to the surface (Figure 7). Te entire procedure mimics thedata-driven case but has two distinct advantages; it doesn’t re-quire a dense shot sampling on the surface, and it doesn’t require

records at zero offset. It does, however, require a reflectivity and

Figure 11. Migrated comparison of Santos Basin 3D data: (left) before and (right) after model-driven interbed multiple attenuation. (Courtesyof Petrobras)

Figure 10. Kirchhoff comparison of Santos Basin 3D data: (left) before and (right) after data-driven interbed multiple attenuation. (FromHembd et al., 2011)

velocity model.

Application

3D data-driven interbed multiple prediction was performed on2D synthetic data and on 3D real data from the Santos Basin.

With 3 s of water and shallow multiple generators, these data

were well suited to the data-driven interbed algorithm. Te syn-


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thetic data (Figure 8) were generated using an acoustic wave-equation modeling package, a velocity field extracted from the3D real data processing, and an arbitrary density model. Teinterbed multiples were predicted using the water bottom (atapproximately 3 s) and the top of salt horizon (at approximately4 s) as the multiple generators. Figure 9 shows the synthetic re-sults after migration highlighting the impressive attenuation of

the strongly focused interbed multiples beneath the minibasins.Te real data results (Figure 10) were generated using a

single iteration of interbed multiple prediction targeting the water-bottom multiples. Analysis of the postmigration interbedattenuation results shows a considerable reduction in the sub-salt migration noise (attributed to interbed multiples). However,some residual multiple-related events are visible on the real datacross-cutting the base of salt. Although the subtraction is by nomeans perfect, these residuals are most likely related to an im-perfect or incomplete prediction. Interbed multiples generatedfrom the top of salt horizon and from the layered evaporate ho-rizons remain unaccounted for.

3D model-driven prediction was also performed on the realdata to target the intrasalt multiples (Figure 11). Te reflectiv-ity model was obtained through prestack time migration of afew near offsets and the arbitrary reference horizon was cho-sen 100 ms below the top salt (hence, considered the principaldownward reflector). All reflectors down to 1 km below top salt

were considered as potential upward generators. A minimumgap separating upward and downward reflectors was also used.Tis corresponds to the shortest leg of the modeled multiple andis needed to avoid modeling primary signal. After modeling, aconservative 3D adaptation and subtraction was performed inthe offset domain. Te results (Figure 11) show a promising at-tenuation of some events cross-cutting the base of salt and slight-ly improved continuity on some targeted presalt events.

Conclusion

In this paper, we have demonstrated the impact of interbedmultiple attenuation on synthetic and real data from the SantosBasin. It has been shown that clearer images of the reservoirzone can be obtained by attenuating the influence of shallower,internal multiple-generating horizons in an informed and de-liberate fashion. Te choice between data-driven and model-driven prediction is shown to be one of convenience with bothmethods performing admirably.

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Acknowledgments: e authors thank CGGVeritas and Petrobras for permission to publish this work, and Antonio Pica, Nicolas Chazal-noel, and Chu-Ong Ting for their helpful contributions.

Corresponding author: [email protected]

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Interbed Multiple Attenuation