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

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

3. Introduction Multiple classification.Multiple classification. Free-surface multiples (FSM).Free-surface multiples (FSM). - Delft, multiple series theories, etc. Water-layer multiples (WLM).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.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. Gaussian beams, Phase-shift, etc. The prediction is emulation.The prediction is emulation. - Part of WLM. - Half is exact; the other half is not exact. Multiple subtraction.Multiple subtraction.

5. Outline Introduction Theory Theory Synthetic experiments Synthetic experiments Application to real data Application to real data Conclusion 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 FWLMMWLM Water bottom LWLM Water surface Subsurface reflector

11. Seismic Data Classification Level 0 Seismic Data (W) Level 1 Upcoming Waves (U)D Level 2 WLMNWLMP Level 3 LWLMFWLMMWLM Note: Converted waves are not considered, and direct waves have been removed.

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

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

14. Synthetic Model Depth (m) (m) 0 1500 Offset (m) 03250 water Sandstone Salt dome Hydrate

15. Synthetic Data Time Time (ms) (ms) 400 2500 2500 Offset (m) 03250

16. Predicted LWLM Time Time (ms) (ms) 400 2500 2500 Offset (m) 03250

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

18. Elimination of RSG & LWLM by Direct Subtraction Time Time (ms) (ms) 400 2500 2500 Offset (m) 03250

19. Further Multiple Attenuation by Deconvolutions Time Time (ms) (ms) 400 2500 2500 Offset (m) 03250

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

21. A Mobil data

22. Predicted LWLM

23. Waveform Comparison

24. WLM Attenuation with Multi-Channel Deconvolution

25. Migration before demultiple Migration after 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 Attenuation with Multi-channel Deconvolution

30. Migration before demultiple Migration after demultiple

31. Outline Introduction Theory Theory Synthetic experiments Synthetic experiments Application to real data Application to real data Conclusion 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 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. 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..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.I am also thankful to 2004 members of UTAM for financial support.