1. Solving Illumination Problems Solving Illumination Problems in Imaging:Efficient RTM & in Imaging:Efficient RTM & Migration Deconvolution Migration Deconvolution J. Yu, J.Hu, M. Zhou, G.T. Schuster & Yi Luo

2. Efficient RTM Motivation Target Oriented RTM Numerical Tests Summary

3. * Motivation: Salt Lens g SALT Uneven Illumination under Salt Uneven Illumination under Salt

4. Expense Accuracy Full-Wave Ray-BeamKirchhoff Migration Accuracy vs $$$ Target RTM No Approx. Multiple Arriv Anti-aliasing Phase-Shift

5. How to Make RTM Efficient Shots at Depths Difference only Along Wavefronts Wavelet Encoding: 5x efficiency

6. OUTLINE Motivation Target Oriented RTM Numerical Tests Summary

7. * Target Oriented RT Migration g SALT Perform FD Solves under Salt Perform FD Solves under Salt Perform Kirchhoff Migration Perform Kirchhoff Migration Above Salt

8. * Compute Kernel by Src at Depth Compute Kernel by Src at Depth r x * r g(s|x) g(x|r) xg(x|r)* g(x|r)g(s|x)*

9. Efficient RTM Motivation Target Oriented RTM Numerical Tests Summary

10. High Velocity Anomaly SEG Salt Dome Model 0 1.5 km 1.5 km/s 2.2 km/s 1.8 m/s 0 3.0 km 0 km 3.0 km

11. Standard FD 0 4.5 km 0 1.5 km Wavefront FD Efficiency: FD along Wavefrojnts

12. FD/ Wavefront FD Cost # Gridpts along side 500 3000 455 FD/ Wavefront FD Cost

13. Model 0 4.5 km 0 1.5 km 0 Wavefront Migration Image 1.5 km/s 2.2 km/s 1.8 km/s

14. Wavefront Migration Image Reverse Time Migration 0 1.5 km 0 4.5 km 0 1.5 km 1.5 km/s 2.2 km/s 1.8 km/s

15. High Velocity Anomaly SEG Salt Dome Model 0 1.5 km 1.5 km/s 2.2 km/s 1.8 m/s 0 3.0 km 0 km 3.0 km

16. Wavefront FD Modeling X (km) Depth (km) 00 0 1010 Wavefront Standard Time = 0.4 s

17. Wavefront FD Modeling X (km) Depth (km) 55 0 1010 Wavefront (leading donuts) Wavefront (rectangular)

18. Reverse-time Images X (km) Depth (km) 2.0 Standard RTM Image Wavefront RTM Image (save 20% CPU time) 5 0 10 15 5 0 10 15 2.5 3.0 3.5

19. X (km) Depth (km) 10 12 WWM image Synthetic Model 8 2.0 2.5 3.0 3.5 10 128 WWM Images

20. X (km) Depth (km) 10 12 Standard RTM image Synthetic Model 8 2.0 2.5 3.0 3.5 10 128 WWM Images

21. Phase Encoding 2x4x 6x10x

23. Summary Efficient RTM Efficient RTM 2. Difference along Wavefront: > 3x 1. Target Oriented RTM: Sources below Salt 3. Phase Encoding: > 3x

24. Solving Illumination Problems Solving Illumination Problems in Imaging:Efficient RTM & in Imaging:Efficient RTM & Migration Deconvolution Migration Deconvolution J. Yu, J.Hu, M. Zhou, G.T. Schuster & Yi Luo

25. Migration Deconvolution Motivation Numerical Tests Summary

26. * Illumination Problems g SALT Gaps in Src & Rec. Shadow Zones. m = (L L ) L d TT

27. Migration Deconvolution Motivation Numerical Tests Summary

28. Solutions of MD vs. LSM m = (L L ) L d TT LSM: T m = (L L ) m’ MD: Migrated image Data

29. Depth (km) 4.5 0 07.0 LSM vs MD 4.5 MD LSM 19 0 X (km)

30. Depth (km) 4.5 0 07.0 LSM vs MD 4.5 MD LSM 19 0 X (km)

31. Migration Deconvolution Motivation Numerical Tests Summary

32. Scatterer Model Kirchhoff Migration Depth (km) 1.8 0 1.0 0 0

33. MD LSM Iter=15 Depth (km) 1.8 0 1.0 0 0

34. Migration Deconvolution Motivation Numerical Tests Summary : 2-D SEG Model

35. Velocity Model 0km 0km15km10km5km0 1500 4500 3000 Depth (m) 6000 2500 Velocity (m/sec)

36. Comparison of Migration and MD Images Prestack Prestack COG COG Migration Migration Image Image 2500-2950 m 2500-2950 m MD Image 0 4 X (km) X (km) 20 20 20 20 Depth (km) Depth (km) 0 0 4 X (km) X (km) 20 20 Depth (km) Depth (km) 0

37. Comparison of Migration and MD Images Prestack COG Migration Image Prestack COG Migration Image 2500 - 2950 m 2500 - 2950 m Prestack COG MD Image Prestack COG MD Image 2500 - 2950 m 2500 - 2950 m X (km) Depth (km) 2 4 2 4

38. KM Depth (km) 4.5 0 07.0 0 X (km) 4.5 0 LSM 15

39. KM Depth (km) 4.5 0 07.0 0 X (km) 4.5 0 MD

40. Depth (km) 4.5 0 07.0 0 X (km) 4.5 0 MD LSM 15

41. MD KM 2 3.5 Depth (km) LSM 19 2 3.5 Depth (km) Zoom View

42. Depth (km) 4.5 0 07.0 Why does MD perform better than LSM ? 4.5 MD LSM 19 0 X (km)

43. Migration Deconvolution Motivation Numerical Tests Summary : Dipping Layers

44. Time (s) 0 2.5 1515 0 CDP 150 X(km) Prestack Migrated COG (45-55) Section Mig + MDMig

45. MD and AVO Amp.Analytical MD Layer 1 Layer 2 Analytical MD 0 angle (deg) 70

46. Migration Deconvolution Motivation Numerical Tests Summary : North Sea

47. Velocity Model 0 25 X (km) 0 4 Time (s) 6 2500 1500 Velocity (m/s)

48. Time Migration Image 0 25 X (km) 0 4 Time (s) 6

49. Migration Deconvolution Image 025 X (km) 0 4 Time (s) 6 MDKM

50. Migration Deconvolution Image 025 X (km) 0 4 Time (s) 6 MDKM

51. 0 3.5 7 20 X (km) Stacked Section WELL

52. Time (s) 1.0 3.0 0.262.01.53.5 1.0 3.0 CDP 150 Offset (km)Velocity (km/s) CDP 150

53. Offset (km) -6.0 -3.5 200800 Shot Number -6.0 -3.5 RMS Amp. before and after preprocessing Raw data After preprocessed 1.442 0.322

54. Time (s) 1.98 2.20 12.1 13.6 1.98 2.20 X (km) Before MD After MD AVO Parameter : P P S S * Reservoir - -3.6 - +2.3

55. B -0.4 0.4 A Crossplot of A and B before MD Near Well A Time interval: 1900-2900 ms

56. B -0.4 0.4 A Crossplot of A and B after MD Near Well A Time interval: 1900-2900 ms

57. B -0.4 0.4 A Crossplot of A and B Based on Wellline log from Well A ( from C.-S. Yin, M.L. Batzle, and C. C. Mosher) Depth: 1900-3100 m

58. Migration Deconvolution Motivation Numerical Tests Summary :G of Mexico

59. Time (s) 0 2 0 15 X (km) Migration Section

60. Time (s) 0 2 0 15 X (km) MD Section

61. Time (s) 0 2.0 015 X (km) AVO Parameter: a*b

62. Time (s) 0 2.0 015 X (km) AVO Parameter: a*b

63. B -0.4 0.4 A Crossplot of A and B before MD CDP: 4797 Time interval: 630-3200 ms

64. B -0.4 0.4 A Crossplot of A and B after MD CDP: 4797 Time interval: 630-3200 ms

65. Migration Deconvolution Motivation Numerical Tests Summary : 3D SEG Salt

66. Inline Velocity Model Offset (km) 09.2 Depth (km) 0 3.8 SALT

67. Comparison of Migration and MD Image 0 468 Y (km) Depth (km) 4 3 2 1 Migration Crossline Section Y (km) 0 468 Depth (km) 4 3 2 1 MD Crossline Section

68. KM Crossline (X,97) Section MD Crossline (X,97) Section 04 2 Depth (km) 118 X (km) 118 X (km) 04 2

69. Conclusions Efficiency MD >> LSMFunction Performanc e Resolution MD = LSM. Suppressing noise MD > LSM Robustness MD < LSM

70. Time (s) 0 3.5 7 20 X (km) Migration Section

71. Time (s) 0 3.5 7 20 X (km) MD Result

72. Time (s) 0 3.5 1218 X (km) Comparison of Mig and MD 1812 X (km) Mig+MDMig Reservoir

73. KM Depth (km) 4.5 0 07.0 0 X (km) 4.5 0 LSM 10

74. Efficient RTM Motivation Gen. Diffraction Mig. Stack Theory Numerical Tests Focusing Operator from Data Summary

75. * Wave EquationCOG Migration Operators IMPLICATION #3 SALT g(r|x)g(x|s)*

76. Velocity Model 0 km 1.2 km 0 s 1.0 s Offset =.7 km 4.5 km 6 km/s 5 km/s Time (s) Depth (km) X (km)

77. COG Migration 0 km 4.5 km COG COGMigrationOperator 0 km 1.2 km Z=.4 km 0 s 1.0 s 0 s 1.0 s MigrationImage Offset =.7 km

78. Time (s) 0.6 1.8 1 1 0.6 1.8 X(km) Close-up of One CRG Mig + MDMig

79. * Compute Kernel by Src at Depth Compute Kernel by Src at Depth r x * r g(s|x) g(x|r) xg(x|r)* g(x|r)g(s|x)*

80. X (km) Depth (km) Standard RTM Image Synthetic Model 5 0 10 15 5 0 10 15 2.0 2.5 3.0 3.5 Reverse-time Images

81. X (km) Depth (km) 10 12 Standard RTM migration WWM image 8 2.0 2.5 3.0 3.5 10 128 WWM Images

82. m = (L L ) L d TT Least Squares Migration Reflectivity Modeling operator Seismic data Migration operator

83. Time (s) 0.5 2.5 1212 0.5 2.5 CDP 150 X(km) Closeup of COG (45-55) Section Mig+ MDMig

84. Frequency (Hz) 0.0 60 100110100110 0.0 60 CDP 150 Trace No. Spectrums of Mig and MD Images Mig + MDMig