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Applications of interbed multiple attenuation
Malcolm Griffiths, Jeshurun Hembd, and Hervé Prigent, CGGVeritas
S
urface-related multiple attenuation schemes have proven
useful in attenuating a significant proportion of the highamplitude multiple content in seismic data. Although generally
sufficient for exploration purposes, there is growing recognition
that more complicated interbed multiples exist within the data
and that they may interfere with the interpretation of reservoirs
and other areas of interest. In this paper, we examine a datadriven method and a model-driven method for attenuating
internal multiples showing both synthetic and real-world
examples of their application.
Introduction
Interbed multiple contamination has been observed previously
during reservoir studies from the North Sea, Middle East, and
Asia Pacific regions, where shallow carbonates or volcanics generate strong reverberations. Frustrating to all parties, this noise
has, for a long time, been categorized as "too difficult" to address because it resists traditional multiple attenuation methods; the multiples and primaries have similar moveout reducing
the effectiveness of Radon-based methods, the multiples and
primaries have similar frequencies and amplitudes reducing the
effectiveness of predictive filtering, and the multiples and primaries have similar dips reducing the effectiveness of f-k filters,
tau-p demultiple, and migration methods implementing dipdiscrimination techniques during the imaging (for example,
controlled-beam migration).
The discovery of the Tupi/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 imaging challenges in the basin, however, is the existence of high-amplitude interbed multiple contamination across the reservoir. An
example can be seen in Figure 1 which shows a migrated image.
Figure 2 shows near-offset data from a second line in the
Santos Basin. Note that the first surface-related multiple appears
well below the presalt target. Strong interbed multiples, however, contaminate the presalt primaries. The interbed multiples
are generated by a series of strong reflectors above the target.
The water bottom, top of Albian layer, top of salt, and layered
evaporites can all contribute toward generating strong interbed
multiples. The strongest of these multiples appear below syncline structures in the top of salt and layered evaporites, due to a
focusing effect that traps multiple energy in the minibasin (Pica
and Delmas, 2008). The migrated results, Figures 1 and 2b,
show the interbed multiples cross-cutting the reservoir. These
interfere with the interpretation of the base of salt event, the
reservoir, and the presalt faulting.
Following the advent of data-driven surface-related multiple
attenuation by Berkhout et al. (Berkhout and de Graaff, 1982;
Verschuur, 1991; Berkhout and Verschuur, 1998), many authors
have extended the data-driven concept to include interbed multiple attenuation. Many methods based on the work of Jakubowicz (1998) have appeared in the literature, for example Ikelle
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(2004). These all require a two-trace convolution followed by a
single-trace correlation or some combination thereof. Equivalent
model-driven methods also exist based on the same concept, notably from Pica and Delmas (2008). The concept has also been
converted to the inverse data space by Luo et al., (2007). The
implicit limitation of this method is that it predicts only internal multiples associated with a single horizon, typically defined
inside a single layer, predicting internal multiples from the surrounding reflectors that will cross this horizon along their path.
A second extension to the data-driven internal multiple attenuation methods, again pioneered by Berkhout et al., (Berkhout and Verschuur, 1997 and 1999; Berkhout, 1999), utilizes
common focal point transforms (CFP) to partially redatum
(receiver side) and fully redatum the input seismic to the level
of the multiple-generating horizon. The method is similar to
Jakubowicz’s method in that it requires a two-trace convolution followed by a single-trace correlation and predicts only the
multiples associated with a single horizon or layer, but it also
requires two redatuming steps and the associated CFP operators.
Kelamis et al. (2002) and Ala’i et al. (2006) demonstrated this
method on land data highlighting the difficulty associated with
the estimation of the CFP operators.
Yet another method of internal multiple attenuation has
been pioneered by Weglein et al. (1997) using the inverse-scattering series. This method is again fully data-driven but has the
added benefit of being capable of predicting all interbed multiples in a single iteration, (Weglein et al., 2008). Depending
upon the implementation complexity, it is possible to predict
either an approximation to the internal multiples (leading term),
or through an infinite series, the exact internal multiple contribution, (Ramirez, 2008). This method has been previously demonstrated on marine data by Otnes et al. (2004) and on land
data by Fu et al. (2010).
With the exception of these inverse-scattering infinite series,
all these methods rely upon some form of adaptive subtraction
in order to attenuate the internal multiple. This has a plethora
of problems associated with it, foremost of which is potential
primary damage. None of these issues are covered in this paper—see Abma et al. (2002) for a detailed analysis of adaptive
subtraction techniques.
This 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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Figure 2. Interbed multiples in data from the Santos Basin. Note the
strong interbed multiples obscuring the presalt target in the nearoffset data (left). The first surface-related multiple appears at the
bottom of the figure, below the target. The Kirchhoff-migrated image
(right) shows that migration swings crossing the base of the target and
interfering with fault interpretation at the target. (From Hembd et al.,
2011)
Figure 6. Downward extrapolation of the wavefield resulting from
the secondary sources created at stage 2. (From Pica and Delmas,
2008)
Figure 7. Final upward extrapolation. (From Pica and Delmas,
2008)
Figure 3. Prediction of multiples reflecting downward from horizon
1.
Figure 4. Backward extrapolation of the muted input data. (From
Pica and Delmas 2008)
methods for attenuating internal multiples: a 3D data-driven
method derived from Jakubowicz, and the 3D model-driven
method from Pica. The focus is on the interbed multiple contamination at the reservoir level for various exploration interests within the Santos Basin, Brazil. The results, however, are
equally applicable to reservoir-level contamination observed in
the North Sea, Middle East, and Asia Pacific.
Methodology
Many factors influence the effectiveness of the various internal
multiple attenuation algorithms; geological complexity (multiple periodicity) and acquisition design (offset coverage and
spatial sampling) are two key factors. It is then not surprising
that the application of any algorithm should first consider the
input limitations.
The 3D data-driven method, following Jakubowicz, is illustrated in Figure 3. An arbitrary horizon is chosen at a point
deeper than the known multiple-generating horizon. The internal multiples predicted are then limited using various muting
methods but are always those having the first upward reflection
below the user-defined horizon, a downward reflection above
the user-defined horizon, and a final upward reflection below
the user-defined horizon. The prediction is given mathematically in Equation 1.
Figure 5. Previously stored wavefield will illuminate the reflectivity
section. (From Pica and Delmas, 2008)
P212 = P2P1*P3
(1)
P1 refers to the wavefields from the surface to the multiple-generating horizons, P2 refers to the source-side wavefield
reflecting from below the multiple-generating horizon, and P3
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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)
Figure 9. RTM comparison of the Santos Basin synthetic data: (left) before and (right) after data-driven interbed multiple attenuation. (From
Hembd et al., 2011)
refers to the receiver-side wavefield reflecting from below the
multiple-generating horizon. P1* refers to the complex conjugate of P1. Equation 1 neglects both the source term and the
surface reflectivity which can both be compensated for using a
least-squares matching filter during the subtraction stage. It also
includes both primary and higher-order multiple contributions
to the interbed multiple prediction. The task of localized wavelet
matching falls upon the subtraction.
Without knowledge of the downward reflection point, however, it becomes necessary to sum Equation 1 over a user-defined
aperture. The method places a high reliance upon the input acquisition density. For accuracy, it requires shot and receiver stations at all possible surface locations as well as measurements
of the wavefield at zero offset. Not surprisingly, these are the
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same limitations found in the surface multiple implementation
and, as such, the method is found to work reasonably well in
deeper water where the near offsets can be easily extrapolated
and in wide-azimuth acquisitions where the azimuthal sampling
is closer to ideal.
The 3D model-driven method also follows the logic of
Jakubowicz. In it, the input data are backward-propagated
though a predefined model to the user-defined arbitrary horizon
(Figure 4). The horizon is defined in the same way as in the
data-driven method. Prior to the back-propagation, the input
data must first be muted to remove all reflections above the target horizon. Once at the target horizon, the wavefield is upward
extrapolated through the model to illuminate the shallow events,
thus identifying the downward reflection points in Figure 5.
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Figure 10. Kirchhoff comparison of Santos Basin 3D data: (left) before and (right) after data-driven interbed multiple attenuation. (From
Hembd et al., 2011)
Figure 11. Migrated comparison of Santos Basin 3D data: (left) before and (right) after model-driven interbed multiple attenuation. (Courtesy
of Petrobras)
For each possible reflection point, the wavefield is downward
propagated through the model a second time until it reaches the
reflecting horizons (Figure 6) where it is finally forward propagated to the surface (Figure 7). The entire procedure mimics the
data-driven case but has two distinct advantages; it doesn’t require a dense shot sampling on the surface, and it doesn’t require
records at zero offset. It does, however, require a reflectivity and
velocity model.
Application
3D data-driven interbed multiple prediction was performed on
2D 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. The synAugust 2011
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thetic data (Figure 8) were generated using an acoustic waveequation modeling package, a velocity field extracted from the
3D real data processing, and an arbitrary density model. The
interbed multiples were predicted using the water bottom (at
approximately 3 s) and the top of salt horizon (at approximately
4 s) as the multiple generators. Figure 9 shows the synthetic results after migration highlighting the impressive attenuation of
the strongly focused interbed multiples beneath the minibasins.
The real data results (Figure 10) were generated using a
single iteration of interbed multiple prediction targeting the
water-bottom multiples. Analysis of the postmigration interbed
attenuation results shows a considerable reduction in the subsalt migration noise (attributed to interbed multiples). However,
some residual multiple-related events are visible on the real data
cross-cutting the base of salt. Although the subtraction is by no
means perfect, these residuals are most likely related to an imperfect or incomplete prediction. Interbed multiples generated
from the top of salt horizon and from the layered evaporate horizons remain unaccounted for.
3D model-driven prediction was also performed on the real
data to target the intrasalt multiples (Figure 11). The reflectivity model was obtained through prestack time migration of a
few near offsets and the arbitrary reference horizon was chosen 100 ms below the top salt (hence, considered the principal
downward reflector). All reflectors down to 1 km below top salt
were considered as potential upward generators. A minimum
gap separating upward and downward reflectors was also used.
This corresponds to the shortest leg of the modeled multiple and
is needed to avoid modeling primary signal. After modeling, a
conservative 3D adaptation and subtraction was performed in
the offset domain. The results (Figure 11) show a promising attenuation of some events cross-cutting the base of salt and slightly improved continuity on some targeted presalt events.
Conclusion
In this paper, we have demonstrated the impact of interbed
multiple attenuation on synthetic and real data from the Santos
Basin. It has been shown that clearer images of the reservoir
zone can be obtained by attenuating the influence of shallower,
internal multiple-generating horizons in an informed and deliberate fashion. The choice between data-driven and modeldriven prediction is shown to be one of convenience with both
methods performing admirably.
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Acknowledgments: The authors thank CGGVeritas and Petrobras for
permission to publish this work, and Antonio Pica, Nicolas Chazalnoel, and Chu-Ong Ting for their helpful contributions.
Corresponding author: malcom.griffiths@cggveritas.com