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Waveform Inversion Techniques for High-Resolution Crosswell Data Analysis

This study from the University of Utah's Geology and Geophysics Department explores the use of waveform inversion for crosswell data, aiming to achieve high-resolution imaging of subsurface structures. The presentation covers key motivations, objectives, theoretical frameworks, and presents synthetic model examples. The results indicate that higher resolution tomograms exhibit greater sensitivity to initial models, offering improved insights into lithological variations at a resolution of 2m by 2m. Conclusions emphasize the potential for future applications and testing on 2-D field data.

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Waveform Inversion Techniques for High-Resolution Crosswell Data Analysis

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  1. Waveform Inversion for Crosswell Data M. Zhou Geology and Geophysics Department University of Utah

  2. Outline • Motivation • Objective • Theory • Examples • Synthetic Model 1 • Synthetic Model 2 • Conclusions

  3. Motivation • High resolution 2 m by 2 m • Analyses of lithology

  4. Outline • Motivation • Objective • Theory • Examples • Synthetic Model 1 • Synthetic Model 2 • Conclusions

  5. Objective • High resolution tomogram

  6. fast, insensitive to initial model low resolution (high freq. approx.) high resolution slow, sensitive to initial model Traveltime vs. Waveform • Traveltime Inversion • Waveform Inversion

  7. Traveltime • Waveform • Initial model • High resolution Objective • + • provide initil model

  8. Outline • Motivation • Objective • Theory • Examples • Synthetic Model 1 • Synthetic Model 2 • Conclusions

  9. D s(x) b(x,t) | t=0 f(x,t) Gradient Forward field Residual backward field Theory Waveform inversion: Misfit = S (dobs-dcal(s))2 Residual waveform = *

  10. Outline • Motivation • Objective • Theory • Examples • Synthetic Model 1 • Synthetic Model 2 • Conclusions

  11. X (m) 0 50 km/s 0 6.0 5.0 40 Depth (m) 4.0 3.0 80 Model Model 1: Model 1m X 1m grid 41 shots/geophones 200 Hz Ricker wavelet Shortest wavelength 20 m

  12. X (m) X (m) X (m) 0 0 0 50 50 50 km/s 0 6.0 5.0 40 Depth (m) 4.0 3.0 80 WI50 Model Tomo50 (ray-based) Model 1: Tomograms

  13. X (m) X (m) X (m) 0 0 0 50 50 50 km/s 0 6.0 5.0 40 Depth (m) 4.0 3.0 80 WIF30 + WI20 Model WIF30 Model 1: Tomograms

  14. X (m) X (m) X (m) 0 0 0 40 40 40 80 80 80 0 Time (sec) 0.1 Tomo50 (ray-based) Model WIF30 + WI20 Model 1: Synthetic CSG

  15. Time (s) Time (s) .01 .01 .04 .04 .025 .025 1. Amplitude 0. -1. Ray-based Traveltime Inversion WIF30 1. Amplitude 0. -1. WI50 WIF30 + WI20 Model 1: One trace

  16. Outline • Motivation • Objective • Theory • Examples • Synthetic Model 1 • Synthetic Model 2 • Conclusions

  17. X (m) 0 90 km/s 0 3.6 3.2 100 Depth (m) 2.8 2.4 210 Model Model 2: Model 3m X 3m grid 18 shots / 32 geophones 60 Hz Ricker wavelet

  18. X (m) X (m) X (m) 0 0 0 90 90 90 km/s 0 3.6 3.2 100 Depth (m) 2.8 2.4 210 WIF20 Model WT10 (wave eq.) Model 2: Tomograms

  19. X (m) X (m) X (m) 0 0 0 90 90 90 km/s 0 3.6 3.2 100 Depth (m) 2.8 2.4 210 WIF20 + WI10 Model WIF20 Model 2: Tomograms

  20. X (m) X (m) 0 0 0 100 100 100 200 200 200 X (m) 0 0.1 Time (sec) WT10 Model WIF20 Model 2: Synthetic CSG

  21. Time (s) .04 .12 .08 1. Amplitude 0. -1. Wave Eq. Traveltime (WT) 10 iterations 1. Amplitude 0. -1. WIF20 Model 2: One Trace

  22. Outline • Motivation • Objective • Theory • Examples • Synthetic Model 1 • Synthetic Model 2 • Conclusions

  23. WI vs. Traveltime Inversion: WIF + WI vs. WI: Conclusions • Higher resolution tomograms; • More sensitive to initial model. • Less sensitive to initial model.

  24. Future Work • Test on 2-D field data

  25. Acknowledgements • I am grateful for the financial • support from the members of • the 2001 UTAM consortium.

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