1. Selecting Robust Parameters for Migration Deconvolution University of Utah Jianhua Yu

2. Problem and Goal Outline Main parameter selection Examples Conclusions

3. 2-D Poststack MIG (Unocal) 0.6 Depth (km) MD 2.8

4. 0 3 km 0 3-D Point Scatterer Model 3 km

5. X (km) Amplitude 1 0 3 0 Y (km) MDMIG X (km) 0 3 0 Y (km) 1 km 3 km 5 km Depth Slides

6. X (km) Amplitude 0 3 0 Y (km) MDMIG X (km) 0 3 0 Y (km) 7 km 9 km 10 km Depth Slides

7. Problem: Improving the stability of MD Algorithm Developed a stable MD filterSolution: Unstable MD at some data sets

8. Outline Main parameter selection Examples Conclusions Problem and Goal

9. Prestack Migration Deconvolution Reflectivity Migrated Section MD is to eliminate this blurring influence in migration image by designing MD operator F T M = L L R Mig: F= (L L ) T R = F M MD: Blurring operator

10. PSMD algorithm: Calculating migration Green’s function with geometry, velocity, and depth level Inverted MD fiter by inversion End of loop on iz Velocity cube For iz=1, nz (depth or time slice) Migrated cube Define the MD filter length MD filter length Aperture width variation along the depth Inversion algorithm-regularization (Hu, 2001)

11. Depth Level i N CDP Depth (km) LL N: MD filtering length L: Aperture width parameter Depth Level 1 L Depth Level N

12. Improved PSMD algorithm: End of loop on iz For iz=1, nz (depth or time slice) Define the MD filter lengthCalculating migration Green’s function with the varied aperture width along the depth and associated with geometry, velocity Inverted MD filter by inversion and applied to the migrated image

13. Outline Main parameter selection Examples Conclusions Problem and Goal

14. 0 3 km 0 3-D Point Scatterer Model 3 km 11 X 11 Receivers 11 X 11 Receivers Imaging: dx=dy=50 m dz=100 m 3X3 Sources; 10 km

15. 0 3 X (km) 0 3 Y (km) 0 3 X (km) 0 3 Y (km) 0 3 X (km) 0 3 Y (km) 0 3 X (km) 0 3 Y (km) 0 3 X (km) 0 3 Y (km) 0 3 X (km) 0 3 Y (km) MIG MD Z=1 km Z=3 km Z=5 km Depth Slices

16. 0 3 X (km) 0 3 Y (km) 0 3 X (km) 0 3 Y (km) 0 3 X (km) 0 3 Y (km) 0 3 X (km) 0 3 Y (km) 0 3 X (km) 0 3 Y (km) 0 3 X (km) 0 3 Y (km) MIG MD Z=7 km Z=9 km Z=10 km Depth Slices

17. 0 3 X (km) 0 3 Y (km) 0 3 X (km) 0 3 Y (km) 0 3 X (km) 0 3 Y (km) 0 3 X (km) 0 3 Y (km) 0 3 X (km) 0 3 Y (km) 0 3 X (km) 0 3 Y (km) MIG MD(new) Z=7 km Z=9 km Z=10 km Depth Slices

18. Z=1 km Z=3 km Z=5 km Ky Kx Ky Kx Ky Kx Ky Kx Ky Kx Ky Kx Spectrum of Green’s function (New)Spectrum of Green’s function (Old)

19. Z=7 km Z=9 km Z=10 km Ky Kx Ky Kx Ky Kx Ky Kx Ky Kx Ky Kx Spectrum of Green’s function (New)Spectrum of Green’s function (Old)

20. 0 3 X (km) 0 3 Y (km) 0 3 X (km) 0 3 Y (km) 0 3 X (km) 0 3 Y (km) 0 3 X (km) 0 3 Y (km) 0 3 X (km) 0 3 Y (km) 0 3 X (km) 0 3 Y (km) MD MD (new) Z=1 km Z=3 km Z=5 km Depth Slices

21. 0 3 X (km) 0 3 Y (km) 0 3 X (km) 0 3 Y (km) 0 3 X (km) 0 3 Y (km) 0 3 X (km) 0 3 Y (km) 0 3 X (km) 0 3 Y (km) 0 3 X (km) 0 3 Y (km) Z=7 km Z=9 km Z=10 km MD MD (new) Depth Slices

22. Outline Main parameter selection Examples: 2-D Meandering Model Conclusions Problem and Goal

23. 0 3 km 0 3-D Point Scatterer Model 3 km Source: 5X5 Receiver: 21X 21

24. Model Meandering Stream Model PSDM ImageMD

25. Outline Main parameter selection Examples: 2-D marine data Conclusions Problem and Goal

26. Poststack MIG from Unocal 0.6 Depth (km) MD 2.8

27. MD 0.6 Depth (km) MD 2.8

28. MIG 0.6 Depth (km) MD 2.8

29. P-P PSTM by Unocal 0.5 5 Time (s) MD

30. MIGMD 0.5 5 Time (s)

31. MIG MD

32. Outline Main parameter selection Examples 2-D PS marine data Conclusions Problem and Goal

33. PS PSTM Image ( by Unocal) 0 6 X (km) 0 8 Time (s)

34. 0 6 X (km) 0 8 Time (s) MDPSTMPSTMD

35. 0 6 X (km) 0 8 Time (s) MD PSTM PSTMD

36. Outline Main parameter selection Examples 2-D Land data Conclusions Problem and Goal

37. MD Time (s) Mig

38. Time (s)

40. Outline Main parameter selection Examples 3-D SEG/EAGE data Conclusions Problem and Goal

41. 3-D SEG/EAGE Salt Model 1.0-1.4 km

42. MD (z=1 km)Mig (z=1 km) X (km) 3 10 Y (km) 59.85 X (km)

43. Problem and Goal Outline Main parameter selection Examples Conclusions

44. Conclusions Filter length N=5-11; Parameter that controls aperture width ranges from 0.005-0.04. Varied aperture width in MD with the depth improved the stability of MD Aperture width and filter length in designing MD filter are two key parameters

45. Acknowledgments Thank Alan Leeds for his constructive suggestions and providing challenging data to test our MD in Chevron.Thank Alan Leeds for his constructive suggestions and providing challenging data to test our MD in Chevron. Thank 2002 UTAM sponsors for their financial supportThank 2002 UTAM sponsors for their financial support Thank Aramco, ChevronTaxco, and Unocal for providing the data setsThank Aramco, ChevronTaxco, and Unocal for providing the data sets