Image-based Q tomography using wavefield ...

Image-based Q tomography using wavefield continuation in the presence of strong attenuation anomalies: A case study in Gulf of Mexico Downloaded 11/30/17 to 185.46.212.81. Redistribution subject to SEG license or copyright; see Terms of Use at http://library.seg.org/

Yi Shen*, Stanford University, Christopher Willacy, Vanessa Goh, Shell International E&P Abstract We present a case history to compensate for a strongly absorptive geological body that has presented a challenge for accurate reservoir identification and interpretation. A workflow was designed, using wave-equation migration Q analysis, to recover a Q model and compensate the attenuated seismic image. By absorption quantification, the attenuated zone due to the anomaly is defined by a slope map. The inverted Q model shows that the strong Q updates are co-located with low velocity, align well with the bright structures, and match with the Q model derived from the log data. These results align with the geological interpretation, and give us the confidence in the image compensation using this estimated Q model. The compensation results show that the image is improved in the following ways: the amplitude is compensated; events become sharper; the structures are more coherent; and the high frequencies of the attenuated image are recovered. Introduction Seismic attenuation, typically quantified by a parameter Q, is a notoriously challenging problem for reservoir identification and interpretation in the Gulf of Mexico (GOM), where strong attenuation anomalies are present. Attenuation degrades the seismic image quality by decaying the image amplitude, lowering the image resolution, distorting the phase of events, and dispersing the velocity. These problems impede accurate image interpretation for hydrocarbon production and well positioning. The GOM data used in this study has such an attenuation problem. A strong absorptive body exists in the shallow subsurface and has an irregular shape and a low interval velocity. This complexity reduces the amplitude and phase of deeper events, and essentially creates a shadow zone over the reservoirs, which limits accurate reservoir interpretation. For example, the brightness of the events in the seismic image are well correlated with the fluid content in the reservoir sand. However, the attenuated amplitude brings ambiguity to the fluid identification and causes errors in mapping the reservoir region. Therefore it is valuable to understand and quantify the attenuation effects of this complex-shaped absorption body to create an accurate laterally- and vertically- varying attenuation model. The improvements in image quality using the derived model provide greater confidence for hydrocarbon exploration.

signal-to-noise ratio, diffractions, and complex subsurface structure. In this paper, we propose to use the method developed by Shen et al. (2013, 2014) —wave-equation migration Q analysis(WEMQA) — to tackle the absorption problem in the GOM data. This method has the ability to build a robust Q model and has two major advantages. Firstly, this method is implemented in the image-space, which uses wavefield-continuation compensation migration with Q to stack out noise, focus and simplify events. In addition, it also provides a direct link between the model perturbation and the image perturbation. Secondly, this method uses wave-equation Q tomography to handle the complex wave propagation caused by the irregular shape of the absorption body. Theory and workflow WEMQA (Shen et al., 2013, 2014) is an inversion method that iteratively updates the Q model and compensates the image. The objective of this inversion scheme is to minimize the attenuation effects of the compensated image using the estimated Q model. In this section, we illustrate the workflow by showing three major steps on a 2D inline slice of the GOM data. 1) Absorption effects quantification Looking ahead of Figure 3(a), this figure shows the attenuated imaging of the 2D section at first iteration migrated with a constant Q model (Q=100000). The bright structures (around depth=3000m, between x=30,000m to x=40,000m) are interpreted as the absorption body that is the main cause of the wiped out image below. These structures are the target regions for our Q estimation. This workflow uses the spectral ratio method of Tonn (1991) to quantify the absorption effects. Theoretically, the logarithm of the ratio between attenuated spectra and non-attenuated/reference spectra linearly decreases with increasing frequency. That means the relation between the logarithm of the ratio and the frequencies is a linear line with a negative slope. The value of the slope indicates the intensity of the attenuation. Since WEMQA measures the spectra from the image domain, the frequencies mentioned here are the spatial frequencies, i.e. wavenumber.

Figure 1(a) shows two windowed spectra: 1) attenuated windowed spectra whose window center is at x= 32,000m and depth=5000m, which is believed strongly attenuated by the absorption body above; 2) referenced windowed spectra whose window center is at x= 28000m and depth=5000m, whose horStudies estimating attenuation tomographically have a long history (Kjartansson, 1979; Tonn, 1991; Brzostowski and McMechan, izontal location is relatively far from the attenuation body. This region is considered much less influenced by absorption than 1992; Leggett et al., 1992; Zucca et al., 1994; Quan and Harthe rest of the area in this 2D cross section. The window size ris, 1997; Dasgupta and Clark, 1998; Leaney, 1999; Mateeva, for both spectra is 2000m long in depth and 200m wide in x. 2003; Plessix, 2006; Rickett, 2006, 2007; Reine et al., 2012a,b). The results show that the higher frequencies of the attenuated However, the estimated Q models from these methods may spectra are much more attenuated than the lower frequencies, be unreliable, due to issues such as spectral interference, low

© 2015 SEG SEG New Orleans Annual Meeting

DOI http://dx.doi.org/10.1190/segam2015-5791585.1 Page 3435

phase.

when compared with the reference spectra.

Downloaded 11/30/17 to 185.46.212.81. Redistribution subject to SEG license or copyright; see Terms of Use at http://library.seg.org/

Figure 1(b) shows the logarithm of the ratio between these two spectra versus frequencies. This curve is contaminated with noise especially at the low and high frequencies. However, the middle range of the spectra (0.004 – 0.016 cycles per meter) shows an almost linear negative slope with some localized deviations due to noise. The linear portion of the spectra is used for the slope calculation. The slope represents the average absorption for the image at window center for the perturbed dataset. Figure 2(a) is the slope estimate for every image point that is used as the window center. The window size and frequency range are the same as before. The ratio of each slope is between the windowed attenuated image and the windowed image at x= 28000m in the same depth. The region shown in blue in Figure 2(a) indicates the area strongly attenuated by the absorptive body.

(a)

(b)

Figure 1: (a) Two windowed spectra in frequencies: 1) attenuated windowed spectra whose window center is at x= 32000m and depth=5000m. The window size is 2000m long in depth and 200m wide in x. The region is believed strongly attenuated by the absorption body above. 2) Referenced windowed spectra whose window center is at x= 28000m and depth=5000m. The window size is the same. The horizontal location x= 28000m of this region is relatively far from the anomaly, and it is considered much less influenced by attenuation than the rest of the area in this 2D section. (b)The logarithm of the ratio between the two spectra in Figure 1(a) versus frequencies. 2) Q tomography The wave-equation tomographic operator developed by Shen et al. (2013, 2014) back-projects the slope map to a gradient of Q that can be used to update the current Q model. Figure 2(b) shows the inverted Q model, which aligns well with the bright shallow reflections of the strong absorption anomaly in Figure 3(a). 3) Migration with Q compensation We input the updated Q model into the migration using oneway wave-continuation with Q compensation (Shen et al., 2013, 2014) for absorption quantification in the next iteration. These three steps are run iteratively until the slope map is minimized. Figure 3(b) is the compensated image using the inverted Q model in Figure 2(b). The image under the anomaly shows that the amplitude is partially recovered, and the structures become more coherent, showing higher resolution and corrected

© 2015 SEG SEG New Orleans Annual Meeting

(a)

(b)

Figure 2: (a) The slope map for every image point that is used as the window center. The region shown in blue indicates the area strongly attenuated by the absorption body. (b) The inverted Q model. The strong updates align well with the known strong absorption anomaly.

Results We applied this workflow to the 3D volume of the GOM data to obtain both the inverted Q model and the compensated image. In this section, we demonstrate the effectiveness of the workflow with a representative 2D depth slice of the 3D volume in this section. The initial Q model we used is a constant Q model (Q=100000). Figure 4(a) and Figure 4(b) are the inverted Q model and the velocity for migration, respectively. The strong updates in the inverted Q model nicely matches the shape and location of the low velocity anomaly, supporting the interpretation that this absorption anomaly comes with low velocity. Figure 7(a) is the comparison of the estimated Q and a Q model derived from log data at depths 3000–3200m. This shows a good match between the two methods and verifies the credibility of our results.

DOI http://dx.doi.org/10.1190/segam2015-5791585.1 Page 3436

Downloaded 11/30/17 to 185.46.212.81. Redistribution subject to SEG license or copyright; see Terms of Use at http://library.seg.org/

(a) (a)

(b)

Figure 3: (a) Attenuated image with a constant initial Q model (Q=100000). (b) Compensated image with the inverted Q model shown in Figure 2(b). The image under the anomaly show that the amplitude is partially recovered, and the structures becomes more coherent due to the increased resolution and the corrected phase.

© 2015 SEG SEG New Orleans Annual Meeting

(b)

Figure 4: A 2D slice of the 3D volume: (a) inverted Q model; (b) migration velocity model.

DOI http://dx.doi.org/10.1190/segam2015-5791585.1 Page 3437

Downloaded 11/30/17 to 185.46.212.81. Redistribution subject to SEG license or copyright; see Terms of Use at http://library.seg.org/

Figure 5(a) and 5(b) are the images before and after compensation using the inverted Q model, respectively. Figure 6(a) and 6(b), and Figure 6(c) and 6(d) zoom in the comparison results at two important regions. The results show that the image quality after compensation is improved in the following ways: the amplitude is compensated; events become sharper; the structures are more coherent. The high frequency losses in the attenuated image in Figure 5(a) are recovered in Figure 5(b), as shown in Figure 7(b).

model. The compensation results show that the image is improved in the following ways: the amplitude is compensated; events become sharper; the structures are more coherent; and the high frequency loss in the attenuated image is recovered. These results will be useful for future reservoir characterization and interpretation. Acknowledgments We would like to thank Shell for permission to publish this paper. We would also like to thank Stanford Exploration Project for their technical support.

(a)

(b)

(c)

(d)

(a)

Figure 6: (a) and (c): Zoomed in regions of Figure 5(a); (b) and (d): zoomed in regions of Figure 5(b).

(b)

Figure 5: A 2D slice of the 3D volume: (a) attenuated image with a constant initial Q model (Q=100000). (b) Compensated image with the inverted Q model shown in Figure 4(a).

Conclusion

(a)

(b)

Figure 7: (a) Spectra of the images in Figure 5. (b) The comparison of the estimated Q and the Q model derived from the log data.

We have applied the workflow using WEMQA to the GOM data acquired over a strong absorption body and demonstrated its effectiveness by showing the 2D slice of the 3D results. By absorption quantification, the attenuated zone due to this anomaly is indicated by a slope map. The inverted Q model shows that the strong Q updates are co-located with the low velocity, align well with the bright structures, and match closely with the Q model derived from the log data. These results agree with the geological interpretation, and give us greater confidence in our image compensation using the estimated Q

© 2015 SEG SEG New Orleans Annual Meeting

DOI http://dx.doi.org/10.1190/segam2015-5791585.1 Page 3438

EDITED REFERENCES Note: This reference list is a copyedited version of the reference list submitted by the author. Reference lists for the 2015 SEG Technical Program Expanded Abstracts have been copyedited so that references provided with the online metadata for each paper will achieve a high degree of linking to cited sources that appear on the Web.

Downloaded 11/30/17 to 185.46.212.81. Redistribution subject to SEG license or copyright; see Terms of Use at http://library.seg.org/

REFERENCES

Brzostowski, M. A., and G. A. McMechan, 1992, 3-D tomographic imaging of near-surface seismic velocity and attenuation: Geophysics, 57, 396–403, http://dx.doi.org/10.1190/1.1443254. Dasgupta, R., and R. A. Clark, 1998, Estimation of Q from surface seismic reflection data: Geophysics, 63, 2120–2128. Kjartansson, E., 1979, Constant Q-wave propagation and attenuation: Journal of Geophysical Research Solid Earth, 84, B9, 4737–4748, doi:10.1029/JB084iB09p04737. Leaney, W. S., 1999, Walkaway Q inversion: 69th Annual International Meeting, SEG, Expanded Abstracts, 1311–1314. Leggett, M., N. R. Goulty, and J. E. Kragh, 1992, Study of traveltime and amplitude time-lapse tomography using physical model data: 54th Meeting & Technical Exhibition, EAEG, 248–249, doi:10.3997/2214-4609.201410508. Mateeva, A., 2003, Thin horizontal layering as a stratigraphic filter in absorption estimation and seismic deconvolution: Ph.D. dissertation, Colorado School of Mines. Plessix, R. E., 2006, Estimation of velocity and attenuation coefficient maps from crosswell seismic data: Geophysics, 71, no. 6, S235–S240, http://dx.doi.org/10.1190/1.2345051. Quan, Y., and J. M. Harris, 1997, Seismic attenuation tomography using the frequency shift method: Geophysics, 62, 895–905, http://dx.doi.org/10.1190/1.1444197. Reine, C., R. A. Clark, and M. van der Baan, 2012a, Robust prestack Q-determination using surface seismic data: Part 1 — Method and synthetic examples: Geophysics, 77, no. 1, R45– R56, http://dx.doi.org/10.1190/geo2011-0073.1. Reine, C., R. A. Clark, and M. van der Baan, 2012b, Robust prestack Q-determination using surface seismic data: Part 2 — 3D case study: Geophysics, 77, no. 1, B1– B10, http://dx.doi.org/10.1190/geo2011-0074.1. Rickett, J., 2006, Integrated estimation of interval-attenuation profiles: Geophysics, 71, no. 4, A19– A23, http://dx.doi.org/10.1190/1.2209722. Rickett, J., 2007, Estimating attenuation and the relative information content of amplitude and phase spectra: Geophysics, 72, no. 1, R19–R27, http://dx.doi.org/10.1190/1.2399451. Shen, Y., B. Biondi, R. Clapp, and D. Nichols, 2013, Wave-equation migration Q analysis (WEMQA): EAGE Workshop on Seismic Attenuation, doi:10.3997/2214-4609.20131850. Shen, Y., B. Biondi, R. Clapp, and D. Nichols, 2014, Wave-equation migration Q analysis (WEMQA): 84th Annual International Meeting, SEG, Expanded Abstracts, 3757–3762. Tonn, R., 1991, The determination of seismic quality factor Q from VSP data: A comparison of different computational methods: Geophysical Prospecting, 39, no. 1, 1–27.

© 2015 SEG SEG New Orleans Annual Meeting

DOI http://dx.doi.org/10.1190/segam2015-5791585.1 Page 3439

Downloaded 11/30/17 to 185.46.212.81. Redistribution subject to SEG license or copyright; see Terms of Use at http://library.seg.org/

Zucca, J. J., L. J. Hutchings, and P. W. Kasameyer, 1994, Seismic velocity and attenuation structure of the Geysers geothermal field, California: Geothermics, 23, no. 2, 111– 126, http://dx.doi.org/10.1016/0375-6505(94)90033-7.

© 2015 SEG SEG New Orleans Annual Meeting

DOI http://dx.doi.org/10.1190/segam2015-5791585.1 Page 3440