Wavefield Prediction of Water-layer Multiples

1. Wavefield Prediction of Water-layer Multiples Ruiqing He University of Utah Oct. 2004

2. Outline • Introduction • Theory • Synthetic experiments • Application to real data • Conclusion

3. Introduction • Multiple classification. • Free-surface multiples (FSM). • - Delft, multiple series theories, etc. • Water-layer multiples (WLM). • - Berryhill, Wiggins, et al.

4. Berryhill’s Approach • The prediction of WLM is obtained by propagating the received data once within the water layer. • - Kirchhoff integral, Finite-Difference, • Gaussian beams, Phase-shift, etc. • The prediction is emulation. • - Part of WLM. • - Half is exact; the other half is not exact. • Multiple subtraction.

5. Outline • Introduction • Theory • Synthetic experiments • Application to real data • Conclusion

6. Seismic Wave Representation gS: Ghost-source. s*: Twin-source. f:visit of subsurface once.g: Receiver-side ghosting.

7. Berryhill’s Emulation

8. FSM Prediction Subscript g: Receiver-side ghosts (RSG). Subscript u: Upcoming data that generate RSG.

9. Multiple Classification • Level 1: • Water-Layer Multiple (WLM). • Non-WLM multiples (NWLM). • Level 2 (WLM): • Last reverberation WLM (LWLM). • First reverberation WLM (FWLM). • Middle reverberation WLM (MWLM). • Definition priority. • Water-Bottom-Multiple (WBM).

10. Types of Water-Layer Multiples LWLM FWLM MWLM Water surface Water bottom Subsurface reflector

11. Seismic Data Classification Note: Converted waves are not considered, and direct waves have been removed.

12. LWLM Prediction Data (W) + Upcoming waves (U) f g Downgoing ghosts (D) LWLM - For synthetic data, the operator g, f can be exactly known. By this design, LWLM can be exactly predicted.

13. Outline • Introduction • Theory • Synthetic experiments • Application to real data • Conclusion

14. Synthetic Model 0 water Hydrate Depth (m) Salt dome Sandstone 1500 0 3250 Offset (m)

15. Synthetic Data 400 Time (ms) 2500 0 3250 Offset (m)

16. Predicted LWLM 400 Time (ms) 2500 0 3250 Offset (m)

17. Waveform Comparisonbetween Data & RSG+LWLM Data RSG + LWLM Amplitude 2400 600 Time (ms)

18. Elimination of RSG & LWLMby Direct Subtraction 400 Time (ms) 2500 0 3250 Offset (m)

19. Further Multiple Attenuationby Deconvolutions 400 Time (ms) 2500 0 3250 Offset (m)

20. Outline • Introduction • Theory • Synthetic experiments • Application to real data • Conclusion

21. A Mobil data

22. Predicted LWLM

23. Waveform Comparison

24. WLM Attenuationwith Multi-Channel Deconvolution

25. Migration after demultiple Migration before demultiple

26. A Unocal Data

27. Predicted LWLM

28. Waveform Comparison At a geophone above non-flat water bottom At a geophone above flat water bottom

29. WLM Attenuationwith Multi-channel Deconvolution

30. Migration after demultiple Migration before demultiple

31. Outline • Introduction • Theory • Synthetic experiments • Application to real data • Conclusion

32. Conclusion • Berryhill’s approach does not need to know the source signature, and can be performed in a single shot gather, but the prediction is emulation. • This method improves Berryhill’s approach by making clear classification among WLM, and using receiver-side ghosts to predict LWLM. • This method exactly eliminates LWLM for synthetic data, and successfully suppresses WLM by multi-channel de-convolutions for field data.

33. Thanks • This research is benefited from the discussions with Dr. Yue Wang and Dr. Tamas Nemeth of ChevronTexaco Co.. • I am also thankful to 2004 members of UTAM for financial support.