Dirty salt velocity inversion: The road to a clearer subsalt image

Transcription

1 GEOPHYSICS. VOL. 76, NO. 5 (SEPTEMBER-OCTOBER 2011); P. WB169 WB174, 8 FIGS /GEO Dirty salt velocity inversion: The road to a clearer subsalt image Shuo Ji 1, Tony Huang 1, Kang Fu 2, and Zhengxue Li 1 ABSTRACT For deep-water Gulf of Mexico, accurate salt geometry is critical to subsalt imaging. This requires the definition of both external and internal salt geometries. In recent years, external salt geometry (i.e., boundaries between allochthonous salt and background sediment) has improved a great deal due to advances in acquisition, velocity model building, and migration algorithms. But when it comes to defining internal salt geometry (i.e., intrasalt inclusions or dirty salt), no efficient method has yet been developed. In common industry practices, intrasalt inclusions (and thus their velocity anomalies) are generally ignored during the model building stages. However, as external salt geometries reach higher levels of accuracy, it becomes more important to consider the once-ignored effects of dirty salt. We have developed a reflectivity-based approach for dirty salt velocity inversion. This method takes true-amplitude reverse time migration stack volumes as input, then estimates the dirty salt velocity based on reflectivity under a 1D assumption. Results from a 2D synthetic data set and a real 3D Wide Azimuth data set demonstrated that the reflectivity inversion scheme significantly improves the subsalt image for certain areas. In general, we believe that this method produces a better salt model than the traditional clean salt velocity approach. INTRODUCTION For deep-water Gulf of Mexico, accurate salt geometry is critical to subsalt imaging. This requires the definition of both external and internal salt geometries (Haugen et al, 2009). In recent years, external salt geometry (i.e., boundaries between allochthonous salt and background sediment) has improved due to advances in acquisition, velocity model building, and migration algorithms (Huang and Yu, 2009, Bowling et al, 2010). But when it comes to defining internal salt geometry (i.e., intrasalt inclusions or dirty salt), no efficient method has yet been developed. Due to the complex salt tectonic environment in deep-water Gulf of Mexico, inclusions within salt (dirty salt), are common. Most of them are sutures where salt and sediments are mixed together. Historically, the salt body has been treated as homogeneous in the Gulf of Mexico. In common industry practice, intrasalt inclusions (and thus their velocity anomalies) are generally ignored during the model building stages. Figure 1 shows a good example from the Gulf of Mexico Garden Banks area, where sutures and inclusions are quite obvious in the seismic images. When looking at the subsalt area, we see clearly a shadow zone underneath the dirty salt body located directly above the base of salt event. Subsalt events lose focus and continuity there. Similar observations can be found from other areas, especially when those inclusion bodies are close to the base of salt event. We observed that the base of salt distortion, subsalt event jittering, and subsalt dim zones correlate with dirty salt occurrence. Several approaches have been proposed to solve the problem of dirty salt. Among those, full waveform inversion (FWI) (Tarantola, 1984, Zhang and Wang, 2009) and intrasalt tomography have a lot of potential. Thus far with FWI, we are still trying to learn the limitations of the method, and we believe there is significant room for improvement. Intrasalt traveltime based tomography will yield good results if there are enough reflections within the salt. To our knowledge, the only significant success with this method was in the Brazil Santos Basin, where tomography corrected the velocity for layered evaporites in the salt and thus yielded a superior presalt image (Huang et al., 2009). When it comes to the Gulf of Mexico, however, intrasalt tomography fails to produce a comparable uplift in most areas. The typical size of Gulf of Mexico inclusions is too small for tomography updates, and their sparse spatial distribution poses a poor constraint for global inversion, resulting in incorrectly smeared velocity updates. Manual picking of dirty salt is another alternative. Industry practice shows that significant uplift for subsalt images can be achieved by this method (Schoemann et al., 2010). However, this method requires a clear boundary between inclusions and the background Manuscript received by the Editor 30 November 2010; revised manuscript received 15 March 2011; published online 21 November CGGVeritas, Houston, Texas, USA. Shuo.Ji@cggveritas.com; Tony.Huang@cggveritas.com; Zhengxue.Li@cggveritas.com. 2 British Petroleum, London, UK. Kang.Fu@bp.com; 2011 Society of Exploration Geophysicists. All rights reserved. WB169

2 WB170 Ji et al. salt, and picking horizons for those small bodies is very time consuming. Furthermore, accurately determining the velocity of those inclusions requires velocity scans, making it very expensive for tiny inclusions. In general, it is only practical for solving specific problems. For a better definition of internal salt geometry, we developed a reflectivity-based inversion scheme to update dirty salt velocity. Reflectivity was measured based on a true-amplitude reverse time migration (RTM) (Zhang and Sun, 2009) volume. Using the intrasalt reflectivity, an initial dirty salt velocity was then estimated, followed by iterative migrations to fine tune the inversion. THEORY Reflectivity, or impedance-based inversion, has been a common practice in the oil industry (Tsemahman, 1995). Following the same concept, using a 1D assumption, we developed a velocity inversion scheme based on the reflectivity. Starting from R ¼ ðρvþ inc ðρvþ salt ðρvþ inc þðρvþ salt Figure 1. Typical subsalt image degradation due to dirty salt. Figure 2. A 2D synthetic RTM comparison. Left: Image using the clean salt model. Right: Image using the exact (dirty) model. ð1þ where R is the reflection coefficient, ρ is density, and V is velocity, we can rewrite the equation to get the following expression for V inc V inc ¼ V salt ð1 þ RÞρ salt ð1 RÞρ inc (2) For all those terms on the right side of equation 2, the reflectivity R can be measured from a true-amplitude RTM volume. The salt velocity and density are known, and the only unknown is the density of the salt inclusions. In Gulf of Mexico, we believe most of the inclusions are sediment bodies inside the salt, so we can bypass ρ inc by using an empirical formula for sediment density; for example, Gardner s equation (Gardner et al., 1974) ρ ¼ αv β p. (3) In this paper, we use α ¼ 0.23 and β ¼ 0.25, with ρ in g cm 3, V in ft/s. 2D SYNTHETIC EXAMPLE To confirm the impact of dirty salt, and to test the different approaches we discussed earlier, a 2D synthetic data set was created by forward modeling using a dirty salt model that had many intrasalt inclusions added to salt body. Under the assumption that those inclusions were sediment bodies within salt, all the inclusions had a velocity slower than salt. Their sizes were small, roughly m wide and m thick. The salt density was assigned to those inclusion bodies during forward modeling. A clean salt velocity model with the same external salt geometry was also created. Two RTM volumes were generated with these two models. The image comparison is in Figure 2. We clearly see the impact of the dirty salt on both the base of salt event and subsalt events. The base of salt became more rugose and less focused. When we look at the subsalt, the shadow zones of the intrasalt inclusions are obvious: The amplitude of subsalt events became much weaker and continuity of subsalt events degraded significantly. Some fault-like structures were introduced to the image due to this distortion. The inaccuracy of the velocity model also produced more migration swings. To check if travel-time based tomography can solve the dirty salt problem, RTM subsurface angle gathers were created with the clean salt model and exact dirty salt model. The impact of dirty salt is clear: Ignoring dirty salt introduces curvature variations in RTM angle gathers (Figure 3). Unlike big sediment basins, where the relationship between curvatures in common image gathers (CIGs) and velocity error is fairly simple, the curvatures caused by velocity error from those inclusion bodies are much harder to interpret. Despite the fact that the velocity is too fast in a clean salt model for all the inclusion bodies, we still observe base of salt and subsalt events curving up (in CIGs) right beneath those inclusion bodies. This comes from the fact that only near angle energy will go through those inclusion bodies and is pushed down by a salt velocity which is too fast. The small dimension of those inclusion bodies (compared to input data total offset) also explains the rapid curvature variation along the x-axis, which is a big challenge for

3 Dirty salt velocity inversion WB171 tomography updates. Preliminary ray-tracing based travel-time tomography tests show that the resolution required by small sediment inclusion bodies is very hard to achieve. We further tested reflectivity-based dirty salt inversion on this 2D synthetic data. The RTM image comparison between the clean salt model and inverted dirty salt model can be found in Figure 4. The reflectivity-based dirty salt model produces a base of salt with much better focusing and continuity, recovers the amplitude of those subsalt events, and greatly reduces migration swings. Similar uplift can be observed in the FWI result, where the distortion of base of salt and subsalt events due to ignoring those inclusion bodies is substantially reduced by the FWI model (Figure 5). The comparison between different velocity models can be found in Figure 6. No additional editing to salt horizons has been carried out, and all the models share the same external salt geometry. From top to bottom, we show a clean salt model, a dirty salt model from reflectivity inversion, a dirty salt model from FWI, and an exact model. It is easy to observe both reflectivitybased inversion and FWI catch those major inclusion bodies successfully. 3D WIDE-AZIMUTH REAL DATA EXAMPLE Encouraged by the 2D synthetic result, we further tested our reflectivity inversion scheme on real 3D wide-azimuth data, on areas with many intrasalt inclusions. Figure 7 and Figure 8 show the comparison between a clean salt velocity model and a reflectivitybased inversion model. From the 2D synthetic test, we expected to see the improved focusing and continuity on both base of salt and subsalt events. Indeed, this was the case. The uplift from the improved internal salt geometry can be observed in both examples. In Figure 7, the inverted model removes the discontinuity that existed in the clean salt velocity result, yielding a continuous base of salt event. The better defined salt geometry, both internal and external, leads to a better subsalt image, especially in the areas circled in this example. By removing the sag in the shallow subsalt events, the inverted model yielded a simpler structure with stronger amplitude; for the deep subsalt, the broken events in the clean salt model now connect to each other, giving us much higher confidence in the structure down deep. In Figure 8, the reflectivity-based dirty salt velocity inversion helped to remove the sag in base of salt and produced a much flatter base of salt, which fits well with the surrounding salt geometry. The major uplift for this area from the inverted model is the improved subsalt continuity and more balanced amplitude. Now we can easily map those events across the section. We also want to point out the image change of those inclusion bodies. After the inversion, because the velocity is slower, the inclusion bodies also shrink in size, but in general the intrasalt reflections have better focusing, indicating that the local velocity update is going toward the right direction. Figure 3. A 2D synthetic RTM angle gather comparison. The yellow box in the upper panel shows the location of CIGs. Lower left: CIGs using the clean salt model. Lower right: CIGs using the exact (dirty) model. The incident angle range is from 0 to 60. Figure 4. A 2D synthetic RTM comparison. Left: Image using the clean salt model. Right: Image using the reflectivity-based inverted salt model. Figure 5. A 2D synthetic RTM comparison. Left: Image using the clean salt model. Right: Image using the FWI inverted salt model.

4 WB172 Ji et al. DISCUSSION Figure 6. A 2D salt model comparison. (a) Clean salt model, (b) reflection based inversion dirty salt model, (c) FWI model, and (d) exact model. Velocity models in Figure 7 and Figure 8 share the same color bar. The color bar represents a velocity from 1500 m/s to 4500 m/s. Figure 7. An RTM volume comparison using real wide-azimuth data: (a) image using the clean salt velocity, (b) image using the reflectivity inverted dirty salt velocity, (c) clean salt model, (d) dirty salt model. The salt horizons have been adjusted during iterative migration to match the image; subsalt velocities are the same. One of the benefits of this reflectivity-based inversion method is its ability to catch small scale inclusion bodies. In our 2D example, all the inclusions are fairly small; the dimensions of those bodies are roughly the same order as the wavelength of a 15 Hz wave propagating inside the salt. Tiny as they are, those inclusions have a big impact on base of salt and subsalt imaging. The reflectivity method catches most of those small inclusion bodies, as long as the reflections from those bodies show up in the stack image. For our 2D synthetic test, the main target is to understand/confirm the impact of those inclusion bodies on base of salt and subsalt events, so we keep the external salt geometry the same. In real data, where true external salt geometry is unknown, this reflectivity-based dirty salt inversion actually can help better define the external salt geometry, especially for regions where small inclusion bodies are clustered together. Figure 7 and Figure 8 show a good example, where Figure 7 shows the image along dip direction and Figure 8 shows the image along strike direction. In this real 3D wide-azimuth data test the external salt geometry in this case, the base of salt has been updated based on the dirty salt RTM image. Comparing the clean salt model (Figures_7c and 8c) and dirty salt model (Figures 7d and 8d) in both Figures, the maximum salt thickness change is around 250 meters, which is a fairly small change considering the major salt body is more than 3000 m thick. But the impact of these small salt changes (due to both dirty salt inversion and the base of salt change) on the subsalt image is not small. Our reflectivity-based inversion generally improves internal salt geometry resolution, which in turn improves external salt geometry as well. Now let us discuss the limitation of this method. When we look at equation 2, we have uncertainties in both R and ρ inc. Because this method is reflectivity-based, noises (migration swings, artifacts, etc.) inside salt bodies will affect the measurement of R, and thus impact the accuracy of the inversion. A clean image is the key for this method. In general, there are two types of noise in a migrated volume: input-related or migrationrelated. When the noise comes from the input data, a good preprocessing flow, especially the denoise and demultiple, is critical. This method works best with wide-azimuth data, as the strong stacking power of wide-azimuth data over noise helps to produce cleaner images for those intrasalt reflectors. This flow tends to work well in regions where external salt geometry is not extremely complex. For regions where external salt geometry has complexities, the accuracy of this method decreases due to uneven distribution of

5 Dirty salt velocity inversion WB173 Figure 8. An RTM volume comparison using real wide-azimuth data: (a) image using the clean salt velocity, (b) image using the reflectivity inverted dirty salt velocity, (c) clean salt model, and (d) dirty Salt Model. The salt horizons have been adjusted during iterative migration to match the image; subsalt velocities are the same. illumination inside salt bodies. The uneven distribution of illumination needs to be considered in two aspects: the spatial distribution at different intrasalt locations, and the angular distribution at different incident angles. The first will apparently affect our amplitude picking, and thus the velocity estimation. The second will cause migration swings, weaken true reflectors inside the salt, and most importantly, invalidate our 1D reflection assumption. Another uncertainty lies in ρ inc. In Gardner s equation, α and β are empirically derived constants that depend on the geology; a single set parameter might not fit a big area. Because we do not always have sonic and density well logs to calibrate those parameters, region by region estimation is recommended, followed by migration to confirm the uplift. Despite the limitations, we believe this method moves one step closer to better salt velocity model building, and our tests, both 2D synthetic and 3D real data, showed improvements in salt definition and subsalt imaging. CONCLUSION As external salt geometries reach higher levels of accuracy, the industry is realizing the importance of internal salt geometry. The effects of salt inclusions, or dirty salt, once believed negligible, actually have a significant impact on subsalt imaging. Ignoring those inclusions leads to degradations in the subsalt image. For regions where many inclusions exist inside salt bodies, the impact of internal salt geometry could be as big as that of external salt geometry. Proper handling of those inclusions is critical for subsalt imaging. Travel time based tomography has a hard time updating velocity for small inclusion bodies with a sparse spatial distribution. Full waveform inversion is still not a common practice in industry due to its high computational cost. Manual picking is human labor intensive. Compared to these approaches, our reflectivity-based dirty salt velocity inversion scheme provides another practical solution. In both 2D synthetic data and 3D real wide-azimuth data tests, this approach has significantly improved the subsalt image for certain areas. We believe the output from this method is, in general, better than the clean salt velocity model used in the industry, and can be combined with other methods to produce an even better result. For example, it can help manual picking determine the velocity for those inclusion bodies, and it can be used as the new starting model for faster FWI converging. The reflectivity-based inversion assumes 1D reflection, which is not always true in reality; however, the method generally works with simple to moderately complex salt bodies. Because 3D wide-azimuth acquisition provides adequate coverage of incident angles, it thus reduces the sensitivity to incident angle, as it works with RTM stack image, which is the summation of all incident angles. Moreover, the key value of the method is that it detects velocity contrast with high spatial resolution, and by recovering the high frequency velocity components within salt body, the base of salt is better defined, which is crucial for subsalt imaging. We believe that this method, simple though it is, has captured some fundamental physics and thus produces a better salt model than the traditional clean salt model approach. ACKNOWLEDGMENTS We want to thank CGGVeritas for permission to publish this work. We want to thank Jerry Young and Yu Zhang for discussion and reviewing. We want to thank Scott Shonbeck and Kristin Johnston for reviewing this work. We want to thank Timmy Dy and Monica Thomas for their contributions to the paper. REFERENCES Bowling, J., S. Ji, D. Lin, D. Chergotis, B. Nolte, and D. Yanchak, 2010, Mad Dog TTI RTM: Better than expected, 80th Annual International Meeting, SEG, Expanded Abstracts, 29, 3313, doi: / Gardner, G. H. F., L. W. Gardner, and A. R. Gregory, 1974, Formation velocity and density The diagnostic basis for stratigraphic traps: Geophysics, 39, doi: / Haugen, J. A., B. Arntsen, and J. Mispel, 2009, Modeling of dirty salt : 79th Annual International Meeting, SEG, Expanded Abstracts. Huang, T., and B. Yu, 2009, Unlocking the Potential of WAZ data at the Tongo discovery with TTI reverse time migration: 79th Annual International Meeting, SEG, Expanded Abstracts. Huang, Y., D. 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