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MD + AVO Inversion

MD + AVO Inversion. Jianhua Yu, University of Utah . Jianxing Hu GXT. Outline. Motivation. Methodology. Numerical Tests. Conclusions. Seismic Trace. G. S. Incorrect Contribution. Migration Ellipse. Layer 1. Layer 2. Actual reflection point.

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MD + AVO Inversion

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  1. MD + AVO Inversion Jianhua Yu, University of Utah Jianxing Hu GXT

  2. Outline Motivation Methodology Numerical Tests Conclusions

  3. Seismic Trace G S IncorrectContribution Migration Ellipse Layer 1 Layer 2 Actual reflection point Amplitude Preserved Prestack Migration + AVO Inversion

  4. Migration noise How to deal with above problems? Footprints due to the coarse acquisition geometry Influences on CRG Image

  5. Motivation MD: • Increase CRG spatial resolution • Reduce prestack migration noise and artifacts • Improve AVO attribute inversion

  6. Outline Motivation Methodology Numerical Tests Conclusions

  7. T m’ = Ld L m d = L m but Data Migrated Section Migration Image m’= True Reflectivity Modelm Prestack Migration

  8. T -1 m = (L L ) m’ Migrated Imaage or CRG Reflectivity Deblurring operator Migration Deconvolution

  9. Velocity analysis and velocity estimate for migration in time domain Prestack migration to generate the migrated common offset (CO) sections Processing Steps: Preprocessing : Filtering, amplitude balancing, and demultiple

  10. Apply MD to common offset sections Normal AVO parameter Inversion Apply MD to AVO section Processing Steps:

  11. Outline Motivation Methodology Numerical Tests Conclusions

  12. Outline Motivation Methodology Numerical Tests Synthetic data Field marine data Conclusions

  13. 0 0 Mig MD 2.5 2.5 CO (45-55) Section X(km) X(km) 1 5 1 5 CDP 150 Time (s)

  14. 0.5 0.5 Mig MD 2.5 2.5 Closeup of COG (45-55) Section X(km) X(km) 1 2 1 2 CDP 150 Time (s)

  15. 0.6 0.6 1.8 1.8 Mig MD Closeup of One CDP Gather X(km) X(km) 1 1.8 1 1.8 Time (s)

  16. Spectrums of Mig and MD Images 0.0 0.0 Mig MD 60 60 Trace No. Trace No. 100 110 100 110 CDP 150 Frequency (Hz)

  17. Outline Motivation Methodology Numerical Tests Synthetic data Field data I Conclusions

  18. 1.0 1.0 3.0 3.0 Offset (km) Velocity (km/s) 0.26 2.0 1.5 3.5 CDP 150 CDP 150 Time (s)

  19. RMS Amplitudes Shot Number 200 800 -6.0 Offset (km) Raw data -3.5 -6.0 -3.5 After preprocessed

  20. Offset (km) Offset (km) Offset (km) 0.26 0.26 0.26 2.0 2.0 2.0 0.0 3.0 Raw data Demultiple Multiples Time (s)

  21. 0.0 0.0 raw data Demultiple 3.0 3.0 Offset (km) Offset (km) 0.26 2.0 0.26 2.0 Time (s)

  22. 0.0 0.0 raw data Demultiple AVO ? 3.0 3.0 Offset (km) Offset (km) 0.26 2.0 0.26 2.0 Time (s)

  23. Well Vrms Well Vint Estimated Vrms Comparison of Estimated RMS Velocity and Well Sonic Data Time (s) 0 3 5 Velocity (km/s) 1

  24. Before MD Zone of interest Mig Mig+MD - +2.3 Zone of interest After MD - -3.6 MD Result in Time Domain 13.6 12.1 X (km) 20 7 0 Time (s) 3.5

  25. Crossplot of A and B before MD 0.4 Near Well A B Time interval: 1900-2900 ms -0.4 -0.4 0.4 A

  26. Crossplot of A and B after MD 0.4 Near Well A B Time interval: 1900-2900 ms -0.4 -0.4 0.4 A

  27. Crossplot of A and B Based on Well log from Well 1(from C.-S. Yin, M.L. Batzle, and C. C. Mosher) 0.4 B Depth: 1900-3100 m -0.4 -0.4 0.4 A

  28. After MD Before MD Well Data 0.4 B -0.4 -0.4 0.4 -0.4 A 0.4 -0.4 A 0.4 A

  29. Outline Motivation Methodology Numerical Tests Synthetic data Field data II Conclusions

  30. Amplitude within Geometry Map Shot Number 1 800 177 0.03 Receiver Number 0.005 1

  31. Velocity Scanning of CMP Gathers 0 Time (s) CDP 4600 8 CDP 4750 CDP 4850 CDP 5000 CDP 5500 CDP 6000

  32. Before MD After MD Multiple Multiple CRG before and After MD Offset (km) 0.5 Before MD Time (s) After MD 1.3

  33. AVO Parameter: A*B X (km) 0 15 0 Time (s) 2.0 Before MD

  34. AVO Parameter: A*B X (km) 0 15 0 Time (s) 2.0 After MD

  35. Outline Motivation Methodology Numerical Tests Conclusions

  36. Improves stratigraphyic resolution Attenuate migration noise and artifacts Help to identify lithology anomaly in AVO attribute sections Conclusions

  37. Amplitude fidelity is still explored when designing MD operator Conclusions

  38. Acknowledgments • Thank Alan Leeds for his constructive comments and suggestions. • Thank ChevronTxacoandWesternGeco for providing the data sets • Thank UTAM sponsors for their financial support

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