1. Improve Migration Image Quality by 3-D Migration Deconvolution Jianhua Yu, Gerard T. Schuster University of Utah

2. Motivation Outline Migration Deconvolution Examples Conclusions Implementation of MD

3. Migration noise and artifacts Seismic Migration Noise 0.5 3.5 Depth (km) Footprint Weak illumination

4. Limit recording aperture What Affects Seismic Migration Quality Irregular acquisition geometry Incorrect velocity High order phenomenon: anisotropy, attenuation etc. Bandlimited wavelet

5. Noise and artifacts Migration Image suffers from Poor spatial resolution Non-uniform illumination

6. Objective : Develop 3-D migration deconvolution Limit recording aperture Irregular acquisition geometry to deblur the influence of

7. Objective : Improving spatial resolution Enhancing illumination Suppressing migration noise and artifacts

8. Motivation Outline Migration Deconvolution (MD) Examples Conclusions Implementation of MD

9. m = G d T but d = G R Migrated Section Data G R m = PSF(R) m = PSF(R) Migration image = Blurred image of Migration image = Blurred image of true reflectivity model true reflectivity model Migration operator Migration Deconvolution Theory

10. m  R GG T Migration imageReflectivity  Migration Green’s function Migration Deconvolution Theory

11. m  R GG T  GG T ][ GG T ][ 1 1 Migration Deconvolution Theory

12. m  R  GG T ][ 1 Migration Deconvolution Theory Migration Green’s function (Schuster et al., 2000)

13. Migration Deconvolution Theory

14. Lateral shift invariant migration Green’s function Reduction of MD cost --- Reference position of migration Green’s function In wavenumber-space domain:

15. Motivation Outline Migration Deconvolution (MD) Examples Conclusions Implementation of MD

16. MD Implementation Steps: Step 1: Prepare traveltime table Velocity cube Acquisition geometry information Step 2: Calculate the migration Green’s function at the depth Zi

17. Step 3: Obtain MD image at the depth Z i by solving following equation MD Implementation Steps: Step 4: Repeat Steps 2-3 until the maximum depth is finished

18. Motivation Outline Migration Deconvolution (MD) Examples: Synthetic data Conclusions Implementation of MD

19. Recording Geometry : Sources : Receivers

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) MIG MD 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) MIG MD Z=7 km Z=9 km Z=10 km Depth Slices

22. 0 2.5 km 0 Meandering Stream Model 2.5 km 5 x 1 Sources; 11 x 7 Receivers 3.5 km

23. Mig MD Model 0 Y (km) X (km) 2.5 0 Z=3.5 KM

24. VSP Geometry : source 21 x 21; geophone: 12 Depth=1.75 km MigrationMD

25. GOM Velocity Model X (km) Depth (km) 12 0 210

26. X (km) Depth (km) 10 8 4 X (km) 410 MigrationMigration+MD

27. X (km) Depth (km) 10 8.5 410 X (km) 410 MigrationMigration+M D

28. 0 12.2 km 0 3-D SEG/EAGE Salt Model 12.2 km 9 x 5 Sources; dxshot=dyshot=1 km 201 x 201 Receivers Imaging: dx=dy=20 m

29. 3-D SEG/EAGE Salt Model X (km)Y (km) Y=7.12 km

30. Mig z = 1.4 km MD X (km) 3 10 Y (km) 59.85 X (km)

31. Mig (z=1.2 km) X (km) 3 10 Y (km) 59.85 X (km) MD (z=1.2 km)

32. X (km) 020 3 10 Depth (km) Sigsbee2B Model

33. X (km) 020 2.5 10 Depth (km) Mig

34. X (km) 020 2.5 10 Depth (km) MD

35. 5 10 Depth (km) Mig. MD

36. Motivation Outline Migration Deconvolution (MD) Examples: 2-D field data Conclusions Implementation of MD

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

38. 0 6 X (km) 0 8 Time (s) MD PSTM(courtesy of Unocal) PSTMD

39. 0 6 X (km) 3 8 Time (s) MD PSTM(courtesy of Unocal) PSTMD

40. MD Time (s) Mig (courtesy of Aramco)

41. Time (s) Mig (Courtesy of Aramco)MD

42. Mig (Courtesy of Aramco)MD

43. Motivation Outline Migration Deconvolution (MD) Examples: 3-D field data Conclusions Implementation of MD

44. 1.6 s Inline Crossline 3D PSTM (courtesy of Unocal) MD

45. 2.0 s Crossline 3D PSTM (courtesy of Unocal) MD

46. 3.0 Mig in Inline (Courtesy of Unocal) MD Times (s) 1.2

47. Mig (Courtesy of Unocal) MD Inline Number 1901 1 300 Crossline Number Inline Number (2 kft)

48. (3.6 kft) Inline Number 1901 1 265 Crossline Number Inline Number Mig MD

49. Inline Number 190 1.1 7.0 Depth (kft) 90Inline Number1 (Crossline=50) Mig (courtesy of Unocal)MD

50. (crossline 200) 1901 1.1 8.0 Depth (kft) Mig (courtesy of Unocal)MD Inline Number

51. Motivation Outline Migration Deconvolution (MD) Examples Conclusions Implementation of MD

52. Conclusions Suppress migration noise Improve spatial resolution MD cost is related to acquisition geometry V(z) assumption for moderately complex models

53. Acknowledgements UTAM Sponsors SMAART Joint Venture Aramco, Aramco, BP and Unocal