1. 3-D Migration Deconvolution Jianxing Hu, GXT Bob Estill, Unocal Jianhua Yu, University of Utah Gerard T. Schuster, University of Utah

2. Why Do Migration Deconvolution (MD) ? Outline Migration Deconvolution Examples Conclusions Implementation of MD

3. Why Do Migration Deconvolution (MD) ? Outline Migration Deconvolution Examples Conclusions Implementation of MD

4. Migration noise and artifacts Migration Noise Problems 0 3.5 Depth (km) Weak illumination Footprint

5. Purpose of MD Processing: Improving spatial resolution Enhancing illumination Suppressing migration noise and artifacts

6. Why Do Migration Deconvolution (MD) ? Outline Migration Deconvolution Examples Conclusions Implementation of MD

7. M = L T Migration: Migrated image L R L is modeling operator Reflectivity

8. T R = (L L ) M 3-D PRESTACK MD Reflectivity Design an improved MD filter Migrated Section MD is to eliminate the blurring influence in migration image by designing MD operator Goal:

9. Why Do Migration Deconvolution (MD) ? Outline Migration Deconvolution Examples Conclusions Implementation of MD

10. MD Implementation Steps: Step 1: Prepare traveltime table Velocity cube Acquisition geometry information or Use migration timetable

11. Calculate the migration Green’s function MD Implementation Steps: Step 2: Y (km) Depth (km) Depth Level i N L

12. Step 4: Invert MD image at the depth Z i by solving linear equations MD Implementation Steps: Step 5: Repeat Steps 2-4 until the maximum depth is finished

13. Why Do Migration Deconvolution (MD) ? Outline Migration Deconvolution Examples : Synthetic data Conclusions Implementation of MD

14. 0 3 km 0 3-D Point Scatterer Model 3 km 11 X 11 Receivers 11 X 11 Receivers dxg=dyg=0.3 km Imaging: dx=dy=50 m dz=100 m 3X3 Sources; dxshot=dyshot=1.5 km 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 2.5 km 0 Meandering Stream Model 2.5 km 5 X 1 Sources; 11 X 7 Receivers 3.5 km

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

19. 0 12.2 km 0 3-D SEG/EAGE Salt Model 12.2 km 9 X5 Sources; dxshot=dyshot=1 km 201 X 201 Receivers Imaging: dx=dy=20 m

20. 3-D SEG/EAGE Salt Model X (km)Y (km) Y=7.12 km

21. Mig and MD ( z=1.4 km, negative polarity) X (km) 3 10 Y (km) 59.85 X (km) MDMig

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

23. MD (z=1.2 km)Mig (z=1.2 km)

24. Why Do Migration Deconvolution (MD) ? Outline Migration Deconvolution Examples: 2-D field data Conclusions Implementation of MD

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

26. 0 6 X (km) 0 8 Time (s) MD PSTM(courtesy of Unocal) PSTMD

27. 0 6 X (km) 3 8 Time (s) MD PSTM(courtesy of Unocal) PSTMD

28. Why Do Migration Deconvolution (MD) ? Outline Migration Deconvolution Examples: 3-D field data Conclusions Implementation of MD

29. 3-D Land Field Data : Receivers : Sources

30. 1.6 s Inline Crossline 3D PSTM (courtesy of Unocal) MD

31. 2.0 s Crossline 3D PSTM (courtesy of Unocal) MD

32. 3 Mig in Inline (Courtesy of Unocal) MD

33. Mig MD Mig MD

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

35. Fault

36. (3.6 kft) Inline Number 1901 1 265 Crossline Number Inline Number Mig (Courtesy of Unocal) MD

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

38. (crossline 200) 1901 1.1 8.0 Depth (kft) Mig (courtesy of Unocal)MD

39. 1250 1.1 7.0 Depth (kft) Crossline Number 7.0 1.1 (Inline =50) Mig ( Unocal ) MD

40. Why Do Migration Deconvolution (MD) ? Outline Migration Deconvolution Examples Conclusions Implementation of MD

41. Conclusions Aperture width and filter length in designing MD filter are two key parameters Improve resolution and suppress migration artifacts MD cost is related with acquisition geometry

42. Acknowledgments Thank Amramco, Unocal, and Chevron- Texaco for providing the data setsThank Amramco, Unocal, and Chevron- Texaco for providing the data sets Thank 2002 UTAM sponsors for their financial supportThank 2002 UTAM sponsors for their financial support The help and comments from Alan Leeds and George Yao are very appreciatedThe help and comments from Alan Leeds and George Yao are very appreciated http://utam.gg.utah.eduhttp://utam.gg.utah.edu