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Natural Multi-Arrival Datuming of CDP Data to VSP Data with VSP Green’s Function

Natural Multi-Arrival Datuming of CDP Data to VSP Data with VSP Green’s Function. Xiang Xiao UTAM, Univ. of Utah Feb 2, 2006. This is not an Outline…. But an content !. Motivation p. 3~5 Theory p. 6~12 Numerical Tests p. 13~24 Conclusion p. 25. Motivation.

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Natural Multi-Arrival Datuming of CDP Data to VSP Data with VSP Green’s Function

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  1. Natural Multi-Arrival Datumingof CDP Data to VSP Data with VSP Green’s Function Xiang Xiao UTAM, Univ. of Utah Feb 2, 2006

  2. This is not an Outline… But an content ! • Motivation p. 3~5 • Theory p. 6~12 • Numerical Tests p. 13~24 • Conclusion p. 25 Motivation Theory Numerical Tests Conclusion

  3. I. Why we need more VSP? CDPVSP • Surface related statics • Twice Once Seabed • Overburden velocity error • Twice Once • Raypath Salt • Longer Shorter • Attenuation • More Less • Frequency Target • Lower Higher • Resolution • Lower Higher Motivation Theory Numerical Tests Conclusion

  4. How to naturally get more VSP? 3DCDP 3D VSP Low folds Naturally datuming ! High folds ! CDP + VSP RVSP! 3D RVSP Motivation Theory Numerical Tests Conclusion

  5. CDP, VSP Well log High folds ! Salt CDP + VSP RVSP! 3D RVSP Use it, or lost it… Better Geologic interpretation ! Better image under the salt ! Motivation Theory Numerical Tests Conclusion

  6. VSP CDP CDP VSP RVSP S2 ∂G(B|A)* ∂ G(x|A) A G(B|A)*]dA G(x|A)- x G(B|x) ~ [ ~ ∂nx ∂nx S2 S1 B II. How to Naturally Redatum ? 1. Start point---Green theorem 2. Problem: CDP + VSP  RVSP Green’s Theorem VSP VSP VSP VSP CDP ∂G(A|B) ∂ G(B|x) G(A|B)]dB G(B|x) - G(A|x) = [ ∂nx ∂nx S1 Replaced by G(B|A)*, switch other terms Motivation Theory Numerical Tests Conclusion

  7. VSP CDP CDP VSP CDP VSP RVSP ∂G(B|A)* ∂ G(x|A) G(x|A) G(B|A)*]dA G(B|A)* G(x|A)- G(B|x) ~ [ ~ ∂nx dA ∂nx S2 CDP VSP RVSP d(B,x) ~ G(x|A) G(B|A) II. Hard to understand ? S2 A x RVSP G(B|x) ~ ~ B S2 S1 It’s Crosscorrelation Datuming ! ~ Motivation Theory Numerical Tests Conclusion

  8. VSP CDP CDP VSP RVSP ∂G(B|A)* ∂ G(x|A) G(B|A)*]dA G(x|A)- G(B|x) ~ [ ~ ∂nx Forward Propagate Backward Propagate ∂nx ( 2+K2)G(B|A)=d(B-A) S2 ReceiverB ReceiverB Source A Source X II. Let’s understand this… Problem: CDP + VSP  RVSP G(B|A)* is the acausal Green’s function S2 A x G(B|x) is the causal Green’s function B S1 Motivation Theory Numerical Tests Conclusion

  9. VSP CDP CDP VSP RVSP ∂G(B|A)* ∂ G(x|A) G(B|A)*]dA G(x|A)- G(B|x) ~ [ ~ ∂nx ∂nx S2 II. Let’s understand this… Problem: CDP + VSP  RVSP Green’s Theorem Back- For- X A B S2 A CDP VSP x For- X B RVSP B S1 Motivation Theory Numerical Tests Conclusion

  10. DipoleFormula VSP CDP CDP VSP RVSP S2 ∂G(B|A)* ∂ G(x|A) A G(B|A)*]dA G(x|A)- x G(x|B) ~ [ ~ ∂nx ∂nx S2 B Solution CDP + VSP  RVSP Multi-arrival Monopole Datuming CDP VSP cosq RVSP i k G(B|A)* G(x|A) G(x|B) ~ dA ~ S1 G(B|A) G(B|A)* + e VSP VSP Deconvolution Datuming ! Motivation Theory Numerical Tests Conclusion

  11. DipoleFormula VSP CDP CDP VSP RVSP ∂G(B|A)* ∂ G(x|A) G(B|A)*]dA G(x|A) + G(x|B) ~ [ ~ ∂nx ∂nx S2 Solution CDP + VSP  RVSP S2 A q x Shortest-arrival Monopole Approximation q VSP B CDP -iwtxB RVSP e S1 cosq i k G(x|A) G(x|B) ~ [ dA ] ~ S2 4π Motivation Theory Numerical Tests Conclusion

  12. What is the benefit ? CDP + VSP  RVSP • Sources are closer to the target; • Higher fold virtual RVSP data are obtained; • No velocity model is needed; • Multi-arrival are considered; Salt Motivation Theory Numerical Tests Conclusion

  13. Outline Comes Back… • Motivation • Theory • Numerical Tests • SEG/EAGE Salt Model • Field Data Examples • Conclusion Motivation Theory Numerical Tests Conclusion

  14. P-wave velocity model Velocity (m/s) 0 4500 Depth (m) 15700 1500 -7850 Offset (m) 7850 A Salt Model ! Motivation Theory Numerical Tests Conclusion

  15. P-wave velocity model Velocity (m/s) 0 4500 Depth (m) 15700 1500 -7850 Offset (m) 7850 CDP Data Geometry… CDP Motivation Theory Numerical Tests Conclusion

  16. Synthetic CDP CSG 0 Time (s) 6 -2000 2000 Offset (m) Data Time (s) Motivation Theory Numerical Tests Conclusion

  17. P-wave velocity model Velocity (m/s) 0 4500 Depth (m) 15700 1500 -7850 Offset (m) 7850 VSP Geometry… Motivation Theory Numerical Tests Conclusion

  18. Synthetic CDP CSG Synthetic VSP CRG 0 0 Time (s) 6 6 -7850 7850 -7850 7850 Offset (m) Offset (m) Data Time (s) Motivation Theory Numerical Tests Conclusion

  19. Synthetic RVSP CSG 0 0 Time (s) Time (s) 6 6 -7850 7850 Offset (m) noise Which is better? Multi-arrival datuming vs. Shortest-arrival datuming 0 Time (s) noise 6 -7850 7850 Offset (m)

  20. How about the waveform? Traces comparisons Direct waves are cut Shortest-arrival datuming poor data folds multi-arrival datuming Normalized Amplitude Zoom area 6 3 Time (s) Motivation Theory Numerical Tests Conclusion

  21. Multi-arrival datuming wins! Zoom View of Traces Shortest-arrival datuming Normalized Amplitude multi-arrival datuming 3 Time (s) 5.5 Motivation Theory Numerical Tests Conclusion

  22. P-wave velocity model Velocity (m/s) 0 4500 Depth (m) 15700 1500 -7850 Offset (m) 7850 Another Datuming Results Motivation Theory Numerical Tests Conclusion

  23. Shortest-arrival datuming Synthetic RVSP CSG 0 Time (s) 6 Multi-arrival datuming Traces comparisons 0 Normalized Amplitude Time (s) 6 2 6 -2000 2000 Time (s) Offset (m) noise

  24. Multi-arrival still wins ! Traces comparisons Direct waves are cut Shortest-arrival datuming Normalized Amplitude multi-arrival datuming poor data folds 2.5 6 Time (s)

  25. IV. Conclusion • Natural datuming, no velocity model is needed ! • Higher fold virtual RVSP data are obtained ! • Multi-arrival datuming wins over shortes-arrival datuming ! • Better agreement are obtained ! Motivation Theory Numerical Tests Conclusion

  26. Thank you! • Thank the sponsor of the 2005 UTAM consortium for their support.

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