Processing Pitfalls: Astride the Cutting Edge of Technology

1. Part II—Pitfall avoidance Mike Perz, Geo-X Systems Ltd. Processing Pitfalls: Astride the Cutting Edge of Technology

2. Introduction • Pitfall avoidance • Pitfall grab bag • Case studies illustrating pitfall avoidance • Acquisition footprint • AVO gather preparation • Deconvolution • Processing works well in many cases

3. Pitfall avoidance in seismic processing • Not really avoidance, rather detectionand escape in the processing world

4. Introduction • Pitfall avoidance • Pitfall grab bag • Case studies illustrating pitfall avoidance • Acquisition footprint • AVO gather preparation • Deconvolution • Processing works well in many cases

5. Pitfall Grab Bag • Trimming on a multiple • Scale window encroaching on mute • Geometry errors • Anything ground roll • Velocity picking • Fast interbed multiples • …

6. Trim statics example: Offset-dependent maxshift (10ms0ms) P M 200 1700 Offset (m)

7. Trim statics example: maxshift =10 ms at all offsets P M 200 1700 Offset (m)

8. Pitfall Grab Bag • Trimming on a multiple • Scale window encroaching on mute • Geometry errors • Anything ground roll • Velocity picking • Fast interbed multiples • …

9. Event with AVO 1500 0 Offset (m) Mean scaling: input (ideal)

10. Event with AVO 1500 0 Offset (m) Mean scaling: gentle mute Scaling window

11. Event with AVO 1500 0 Offset (m) Mean scaling: harsh mute Scaling window

12. Pitfall Grab Bag • Trimming on a multiple • Scale window encroaching on mute • Geometry errors • Anything ground roll • Velocity picking • Fast interbed multiples • …

13. Pitfall escape/detection Golden Rule: • Understand algorithmic assumptions • Recognize degree to which data conform to assumptions

14. Introduction • Pitfall avoidance • Pitfall grab bag • Case studies illustrating pitfall avoidance • Acquisition footprint • AVO gather preparation • Deconvolution • Processing works well in many cases

15. N Time-interval map at target level

16. N Far-offset fold at target level 30 18

17. Compare: time-interval map at target level N

18. Pitfall detection guideline: • Seek spatial correlation between independent data attributes

19. 2700 0 Offset (m) Aside: Footprint explanation COFF used for simulating 1-D earth response

20. N Aside: Footprint explanation Time slice at target from 1-D earth simulation

21. N Time slice near ZOI structure stack

22. N Compare: time slice near ZOI after binbal

23. Reduced S/N Smearing due to bin borrowing Poor offset distribution Pitfall escape guideline #1: • Be prepared to stray from “accepted” flows

24. Introduction • Pitfall avoidance • Pitfall grab bag • Case studies illustrating pitfall avoidance • Acquisition footprint • AVO gather preparation • Deconvolution • Processing works well in many cases

25. Results after “AVO-friendly” processing Offset (m) CMP gathers 1500 0 Product (Intercept*Gradient) stack

26. Shot before F-K filtering - 20 K (cycles/1000m) 40 0 300 ms Freq (Hz) 100 1500 Offset (m) 0

27. Shot after F-K filtering - 20 K (cycles/1000m) 40 0 300 ms Freq (Hz) 100 1500 Offset (m) 0

28. 0 Offset (m) 1500 Synthetic shot before F-K filtering 40 K (cycles/1000m) - 30 0 Freq (Hz) 100

29. 0 Offset (m) 1500 Synthetic shot after F-K filtering 40 K (cycles/1000m) - 30 0 Mute zone Freq (Hz) 100

30. F-X noise attenuation: synthetic test Processed CMP Gather Input CMP Gather AVO Offset (m) 0 1500

31. F-X synthetic test: Plot of peak amplitudes

32. Result after AVO-unfriendly processing (F-X, F-K filtering) Offset (m) CMP gathers 1500 0 Product (Intercept*Gradient) stack

33. Compare: AVO-friendly processing Offset (m) CMP gathers 1500 0 Product (Intercept*Gradient) stack

34. Amplitude distortion due to noise attenuation Noise in data Pitfall escape guideline # 1: • Be prepared to stray from “accepted” flows

35. Introduction • Pitfall avoidance • Pitfall grab bag • Case studies illustrating pitfall avoidance • Acquisition footprint • AVO gather preparation • Deconvolution • Processing works well in many cases

36. Two N-S lines reveal lateral wavelet instability Line 40 Line 10

37. Bad shot Autocor Shot record Amp spec 0 Freq (Hz) 50

38. Amplitude map, target level N-S line 40 N-S line 10

39. N Shot-averaged NCCF 0.20 0.46

40. N Drift thickness 63 m 130 m

41. N Residual shot phase spectra at 13 Hz -24° 22°

42. Pitfall detection guideline: • Seek spatial correlation between independent data attributes

43. Input shots Input shots CMP gather CMP gather Stack Stack 16 Hz Notch filter 16 Hz Notch filter Spiking decon SC decon SC decon F-K filter F-K filter Unstable wavelets Troubleshooting strategy: strip down to “Plain Jane” flow Operator length? Prewhitening? Zero phase decon? Conclude: problem lies with decon operator minimum phase estimate Wavelet stability improved Unstable wavelets

44. Input shots CMP gather Stack Tr x tr zero phase decon 16 Hz Notch filter SC decon F-K filter Unstable wavelets Counterexample: don’t strip down to “Plain Jane” flow Unreliable min phase estimate? SC solution indeterminacy problems? Data don’t fit SC model? Interaction effects between SC decon and F-K or notch filter? Wavelet stability improved

45. Pitfall escape guideline #2: Use KISS principle when troubleshooting • Strip down flow to “bare bones” then systematically tweak parameters • Saves time in testing • Ensures “apples to apples” comparisons • Prevents confounding of effects

46. Two inlines after new processing (zero-phase decon) Line 40 Line 10

47. Compare: : Two inlines reveal lateral wavelet instability Line 40 Line 10

48. Failure to remove real earth min phase filters Poor estimates of min phase Pitfall escape guideline #1: • Be prepared to stray from “accepted” flows

49. Introduction • Pitfall avoidance • Pitfall grab bag • Case studies illustrating pitfall avoidance • Acquisition footprint • AVO processing problem • Deconvolution breakdown • Processing works well in many cases

50. N 700 700 t (ms) t (ms) 800 800 900 900 “Feel good” example: mining data set