Pitfalls in poststack data conditioning: Distinguishing enhanced geology from enhanced artifacts

1. AASPI Pitfalls in poststack data conditioning: Distinguishing enhanced geology from enhanced artifacts Bryce Hutchinson, Fangyu Li* AASPI, The University of Oklahoma November 17, 2016

2. A common 3D poststack data conditioning workflow Seismic amplitude Crossline dip Inline dip Similarity Structure-oriented filtering Structure-oriented filtering Spectral balancing   (α=0.04) Reduced noise, Sharper edges Filtered amplitude Reduced noise, Sharper edges Broader bandwidth, Smeared edges! 2

3. Outline • An overview of the geologic objective and data quality • The results of the applied workflow • A review of some theory • Reconciliation of the results with geology • An improved workflow

4. Objectives Determine the best poststack data conditioning workflow to apply to my data volume that: • Suppresses cross-cutting random and coherent noise, • Improves vertical resolution, and • Improves lateral resolution, particularly of fault edges, but does not: • Damage amplitudes for subsequent quantitative interpretation, • Enhance artifacts, • Organize noise, and • Create features that look like geology but are not!

5. Study Area:Taranaki Basin, New Zealand (NZPM 2013)

6. Data Quality Poor reflector continuity Crosscutting migration artifacts Poor reflector continuity

7. Faults in original seismic data A A’ B B’ 0 • Faulting is persistent throughout section • Crosscutting noise gives the appearance of faulting Faults? Or Noise? 1 2 Time (s) Faults? Or Noise? 3 4 km 4

8. 3D Data conditioning No noticeable structural discontinuities Zone of interest red arrow corresponding to red dotted line in timeslice Possible faults highlighted by green arrows 8

9. 3D Data conditioning Zone of interest more broken up Discontinuities appear in zone of interest

10. 3D Data conditioning Discontinuities are sharpened further

11. 3D Data conditioning No considerable changes from 2nd iteration of SOF

12. Quality Control: Inaccurate dip calculation could remove the structural orientation (SO) from the filter (F) Opacity (%) (+) Yellow dip azimuth values showing in high dip zones 0 Seismic Amplitude (-) 0 100 +180 2 Dip Azimuth Dig magnitude transparent at low values Seismic reflectors not visible in high dip zones -180 0 100 8 4 Dip Magnitude 1 km Dip magnitude, azimuth, seismic consistent with expectations Zones of high dip show variable dip azimuth, opaque dip magnitude, and no visible seismic reflectors 0 0 100

13. 3D Data conditioning Anomalies discontinuous, rectilinear Anomalies Continuous, curvilinear SOF window size “Faults” have nonvertical dip and cut multiple horizons Discontinuity patterns are not consistent throughout Geologic “look” of faults, not strictly vertical Not a large window size compared to regional extent of features

14. Some theory:Other than velocities and statics, what causes smeared fault edges? Lecomte et al. (2016) discussed efficient 3D modeling using ray tracing and point-spread functions defined by the illumination. One of their findings is that higher bandwidth yields sharper reflector truncations Are we collapsing the PSF? 250 m (Lecomte, 2016)

15. Some more theory: What is SOF doing? Fehmers and Hoecker (2002) pioneered “anisotropic diffusion” in seismic data to include edge detection, edge preservation, and orientation of a data filter. The ultimate outcome being decreased interpretation times. Too much diffusion – discontinuities lost Reflectors are becoming more continuous Discontinuities are sharpened (Fehmers and Hoecker, 2002)

16. A 2D Kuwahara filter applied to photo Additive “salt and pepper” noise Maintain discontinuities more reliably with Kuwahara filter “Paint by numbers” artifact Kuwahara filtering Kuwahara filtering Suppressed noise Sharp edges “Paint by numbers” (Qi et al., 2016).

17. The impact of spectral balancing • Organization in a geologically reasonable manner • Associated artifacts

18. The impact of spectral balancing SOF sharpening past original spectrum Discontinuities associated with specific frequencies not in the acquisition bandwidth Sharpened fault edges fall above original spectrum!

19. Frequency spectra Original seismic data Second iteration of SOF Spectral balancing of 2nd iteration SOF 0 0 0 1 1 1 2 2 Time (s) 2 Magnitude balancing through spectrum Spectral magnitude becomes more focused 3 Magnitude 3 3 4 4 4 10 20 30 40 50 60 10 20 30 40 50 60 10 20 30 40 50 60 Frequency (Hz) Frequency (Hz) Frequency (Hz)

20. The modified 3D poststack data conditioning workflow Seismic amplitude Crossline dip Inline dip Similarity Structure-oriented filtering Spectral balancing   (α=0.04) Structure-oriented filtering Reduced noise, Sharper edges Filtered amplitude Broader bandwidth, Smeared edges Reduced noise, Sharper edges

21. Conclusions • Multiple iterations of structural oriented Kuwahara filtering increases the lateral wavenumber content in the data. • Like bandwidth extension applied vertically, such an increase in resolution beyond that in the data needs to be validated against known structural deformation styles and well control • Spectral balancing implemented with high frequency limits may remove the previous increase in lateral resolution, suggesting a modification to our workflow • Acquisition footprint can also be enhanced! • Poststack data conditioning are filters. Interpreters need to emulate processor best practices: filter -> QC -> filter -> QC -> filter -> QC again

22. Acknowledgements The many companies supporting the AASPI consortium enable us investigate a broad variety of topics. I would also like to extend thanks to NZPAM for supplying the data for this study. Thank you.

23. Questions