1. Computing Attributers on Depth-Migrated Data Name : Tengfei Lin Major : Geophysics Advisor : Kurt J. Marfurt AASPI,The University of Oklahoma 1

2. Contents 1. MOTIVATION1. MOTIVATION 2. INTRODUCTION2. INTRODUCTION 3. ATTRIBUTE ACCURACY AND RESOLUTION AS A FUNCTION OF THE DATA-ADAPTIVE WINDOW3. ATTRIBUTE ACCURACY AND RESOLUTION AS A FUNCTION OF THE DATA-ADAPTIVE WINDOW 4. DIP COMPENSATION ON SPECTRAL DECOMPOSITION4. DIP COMPENSATION ON SPECTRAL DECOMPOSITION 5. CONCLUSIONS5. CONCLUSIONS 2

3. Contents 1. MOTIVATION1. MOTIVATION 2. INTRODUCTION2. INTRODUCTION 3. ATTRIBUTE ACCURACY AND RESOLUTION AS A FUNCTION OF THE DATA-ADAPTIVE WINDOW3. ATTRIBUTE ACCURACY AND RESOLUTION AS A FUNCTION OF THE DATA-ADAPTIVE WINDOW 4. DIP COMPENSATION ON SPECTRAL DECOMPOSITION4. DIP COMPENSATION ON SPECTRAL DECOMPOSITION 5. CONCLUSIONS5. CONCLUSIONS 3

4. 4 1. Motivation Fault Model

5. 5 PSTM Profile of the Fault Model

6. 6

7. 7 PSDM Profile of the Fault Model

8. Contents 1. MOTIVATION1. MOTIVATION 2. INTRODUCTION2. INTRODUCTION 3. ATTRIBUTE ACCURACY AND RESOLUTION AS A FUNCTION OF THE DATA-ADAPTIVE WINDOW3. ATTRIBUTE ACCURACY AND RESOLUTION AS A FUNCTION OF THE DATA-ADAPTIVE WINDOW 4. DIP COMPENSATION ON SPECTRAL DECOMPOSITION4. DIP COMPENSATION ON SPECTRAL DECOMPOSITION 5. CONCLUSIONS5. CONCLUSIONS 8

9. 2. Introduction stop caution go Interpretation traffic light Attribute comparison of time- vs. depth-migrated data. Attributes on depth migrated data Fault shadows are removed, coherence sees the fault Velocity pull-up and push-down are removed; curvature sees true structure Spectral components tune at the true depth thickness Wavelet spectrum changes with velocity Tuning analysis of dipping layers is in apparent cycles/km Attributes on time migrated data Coherence sees fault shadows as a 2 nd discontinuity Curvature sees velocity pull-up and push-down as structural artifacts Spectral components tune at a given time thickness Wavelet time spectrum unaffected by velocity Tuning analysis of dipping layers is in apparent cycles/s 9

10. Spectral components on depth migrated data Range between 0.5-12.0 cycles/kft at 10,000ft/s Range between 0.0005-.0012 cycles/ft at 10,000ft/s Spectral components on time migrated data Range between 5-120 Hz (cycles/s) stop caution go Interpretation traffic light 10

11. Contents 1. INTRODUCTION1. INTRODUCTION 2. MOTIVATION2. MOTIVATION 3. ATTRIBUTE ACCURACY AND RESOLUTION AS A FUNCTION OF THE DATA-ADAPTIVE WINDOW3. ATTRIBUTE ACCURACY AND RESOLUTION AS A FUNCTION OF THE DATA-ADAPTIVE WINDOW 4. DIP COMPENSATION ON SPECTRAL DECOMPOSITION4. DIP COMPENSATION ON SPECTRAL DECOMPOSITION 5. CONCLUSIONS5. CONCLUSIONS 11

12. 12 Window Size !!!

13. 13 A Review of Coherence Schematic Showing a 2D Search-based Estimate of Coherence

14. 14 Coherence using user-defined window (20ms)

15. 15 Coherence using data-adaptive window (10~40ms)

16. 0 20 0 2 4 Time (s) 0 50 Window size, R (ms) 25 (b) 0 20 Wavenumber, κ (cycles/km) 0 4 8 Depth (km) (c) 0 10 0 Window size, R (m) 50 (d) Peak Frequency, f (Hz, cycles/s) 0 40 (a) 16

17. 17 Seismic Profile

18. 18 Seismic Profile

19. 19 Peak Wavenumber (cycles/km)

20. 20 Coherence Using User-defined Window (50m)

21. 21 Coherence Using Data-adaptive Window (20~100m)

22. 22 5 1 6 4 3 2 7 8 2 km Pos Neg Amp 0 0.0 2.0 4.0 6.0 8.0 Depth (km) Seismic Profile

23. 23 5 1 6 4 3 2 7 8 2 km Pos Neg Amp 0 0 k (cycles/km) 20 0.0 2.0 4.0 6.0 8.0 Depth (km) Peak Wavenumber (cycles/km)

24. 24 5 1 6 4 3 2 7 8 2 km Pos Neg Amp 0 0.4 1.0 Opacity 10 Coh 0.0 2.0 4.0 6.0 8.0 Depth (km) Coherence Using User-defined Window (50m)

25. 25 5 1 6 4 3 2 7 8 2 km Pos Neg Amp 0 0.4 1.0 Opacity 10 Coh 0.0 2.0 4.0 6.0 8.0 Depth (km) Coherence Using Data-adaptive Window (20~100)

26. Contents 1. INTRODUCTION1. INTRODUCTION 2. MOTIVATION2. MOTIVATION 3. ATTRIBUTE ACCURACY AND RESOLUTION AS A FUNCTION OF THE DATA-ADAPTIVE WINDOW3. ATTRIBUTE ACCURACY AND RESOLUTION AS A FUNCTION OF THE DATA-ADAPTIVE WINDOW 4. DIP COMPENSATION ON SPECTRAL DECOMPOSITION4. DIP COMPENSATION ON SPECTRAL DECOMPOSITION 5. CONCLUSIONS5. CONCLUSIONS 26

27.  Spectral decomposition is run on vertical traces  Therefore, we obtain “apparent” vertical spectral components  To use tuning effects to estimate thickness we need to compensate for dip haha >h r CDP No Time (ms) haha haha = h r 27

28. According to tuning phenomenon and the schematic diagram of last slice: 28 +

29. V p =20,000 ft/s h a = 100 ft, V = 10,000 ft/s fafa frfr 400800 12001600 200024002800 ft -400 -800 Depth (ft) 40 60 80 frequency (Hz) 0 0 29 Tuning Thickness Model

30. 0 102030405060708090 Frequency (Hz) Amplitude Dip-corrected frequency Apparent frequency 30 Frequency Spectrum

31. 0.5 1.5 1.0 Time (s) 1 km Pos Neg 0 Opacity 0 1 75 25 f Amp Peak Frequency (Apparent) 31

32. 0.5 1.5 1.0 Time (s) 1 km Pos Neg 0 Opacity 0 1 75 25 f Amp Dip Magnitude 32

33. 0.5 1.5 1.0 Time (s) 1 km Pos Neg 0 Opacity 0 1 75 25 f Amp Peak Frequency (Dip-corrected) 33

34. 34 Peak Wavenumber (Apparent)

35. 35 Dip Factor

36. 36 Peak Wavenumber (Dip-corrected)

37. Contents 1. INTRODUCTION1. INTRODUCTION 2. MOTIVATION2. MOTIVATION 3. ATTRIBUTE ACCURACY AND RESOLUTION AS A FUNCTION OF THE DATA-ADAPTIVE WINDOW3. ATTRIBUTE ACCURACY AND RESOLUTION AS A FUNCTION OF THE DATA-ADAPTIVE WINDOW 4. DIP COMPENSATION ON SPECTRAL DECOMPOSITION4. DIP COMPENSATION ON SPECTRAL DECOMPOSITION 5. CONCLUSIONS5. CONCLUSIONS 37

38. CONCLUSIONS 38 In the presence of strong lateral variations in velocity, time- migration fails to properly image the subsurface. These imaging errors can give rise to attribute artifacts. In the presence of strong lateral variations in velocity, time- migration fails to properly image the subsurface. These imaging errors can give rise to attribute artifacts. Attributes on depth migrated data fault plane reflections may be mistreated as stratigraphic reflections by most attributes. The noise is always higher and may need to be conditioned using structure-oriented filtering prior to attribute computation. With incensement of velocity with depth, the change in wavelength from top to bottom of a survey is much greater than the change in period in time-migrated data. Depth migration is designed to handle complex structure which in many cases implies steep dips.

39. CONCLUSIONS 39 I demonstrate the value of these modifications by applying data adaptive attribute windows to pre-stack time- and depth-migrated data volumes. I demonstrate the value of these modifications by applying data adaptive attribute windows to pre-stack time- and depth-migrated data volumes. Initially, the depth-migrated data were significantly noisier than the time-migrated data, resulting in noisier attribute images. Initially, the depth-migrated data were significantly noisier than the time-migrated data, resulting in noisier attribute images. However, by careful structure-oriented filtering WE were able to generate superior attribute images of faulting that did not suffer from the fault shadow and velocity pull up and push down artifacts found in the time-migrated images. However, by careful structure-oriented filtering WE were able to generate superior attribute images of faulting that did not suffer from the fault shadow and velocity pull up and push down artifacts found in the time-migrated images.

40. We thank the sponsors of the OU Attribute- Assisted Processing and Interpretation Consortium for their financial support. We also thank Sponsors for permission to publish showing their data. 40 ACKNOWLEDGEMENTS

41. 41

42. 42

43. The definition of volumetric dip. (Image made by Marfurt, 2006). 43 3.1 A Review of Volumetric Dip Estimation

44. 44 Schematic showing a 2D dip calculation.

45. 45 Schematic showing a 2D dip calculation.

46. 46 Amplitude

47. 47 Peak Frequency

48. 48 Inline Dip Component Using a User-defined Window (20ms)

49. 49 Inline Dip Component Using a Data-adaptive window (10~40ms)

50. 50 Crossline Dip Component Using a User-defined Window (20ms)

51. 51 Crossline Dip Component Using a Data-adaptive window (10~40ms)