1 / 27

Least Squares Migration of JAPEX Data and PEMEX Data

Least Squares Migration of JAPEX Data and PEMEX Data. Naoshi Aoki. Outline. Theory LSM resiliency to artifacts from poor acquisition geometry LSM image sensitivity to wavelet estimation errors Multi-scale LSM applied to poststack JAPEX data

rupali
Télécharger la présentation

Least Squares Migration of JAPEX Data and PEMEX Data

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Least Squares Migration of JAPEXData and PEMEX Data Naoshi Aoki

  2. Outline • Theory • LSM resiliency to artifactsfrom poor acquisition geometry • LSM image sensitivity to wavelet estimation errors • Multi-scale LSM applied to poststack JAPEX data • Target-oriented LSM applied to poststack PEMEX data • Conclusions

  3. Theory Forward modeling Poststack 2D Syncline Model Kirchhoff Migration Inversion LSM Steepest descent algorithm Ricker wavelet (15 Hz)

  4. Outline • Theory • LSM resiliency to artifacts from poor acquisition geometry • LSM image sensitivity to wavelet estimation errors • Multi-scale LSM applied to poststack JAPEX data • Target-oriented LSM applied to poststack PEMEX data • Conclusions

  5. LSM Resiliency to Artifacts from Poor Acquisition Geometry 3D U Model Model Description Model size: 1.8 x 1.8 x 1.8 km U shape reflectivity anomaly Cross-spread geometry Source : 16 shots, 100 m int. Receiver : 16 receivers , 100 m int. 0 CSG TWT (s) ● Source ● Receiver 5 0 1.8 X (m) U model is designed for testing Prestack 3D LSM with arbitrary 3D survey geometry.

  6. Kirchhoff Migration vs. LSMApplied to the 3D U Model Kirchhoff Migration Images (a) Actual Reflectivity (c) Z = 250 m (e) Z = 750 m (g) Z=1250m LSM Images after 30 Iterations (b) Test geometry (d) Z=250m (f) Z=750m (h) Z=1250m ● Source ● Receiver

  7. Comparison of Images from the Cross-spread Data Actual Reflectivity Image of Y = 500 m Kirchhoff Migration Image LSM Image 0 0 Z (km) 1.8 1.8 0 0 1.8 1.8 X (km) X (km)

  8. LSM Resiliency to Artifacts • Test Summary • LSM showed a significant resiliency to artifacts from poor acquisition geometry. • LSM has an ability to reduce data acquisition expense.

  9. Outline • Theory • LSM resiliency to artifacts from poor acquisition geometry • LSM image sensitivity to wavelet estimation errors • Multi-scale LSM applied to poststack JAPEX data • Target-oriented LSM applied to poststack PEMEX data • Conclusions

  10. LSM Image Sensitivity to Wavelet Estimation Errors • LSM algorithm requires a source wavelet. • I tested LSM image sensitivity to wavelet estimation errors in the following 2 cases : • LSM with correct wavelet, • LSM with a Ricker wavelet (15 Hz).

  11. Actual Model LSM Image with Correct Source Wavelet Data LSM Image 0 0 0 Depth (km) Depth (km) TWT (s) 2 2 2 0 0 0 2 2 2 X (km) X (m) X (km)

  12. Kirchhoff Migration Image Actual Model LSM Image with a Ricker Wavelet (15 Hz) LSM Image 0 0 Depth (km) Depth (km) 2 2 0 0 2 2 X (km) X (km)

  13. LSM Image Sensitivity to Errors in the Source Wavelet • Test Summary • An accurate estimate of the source wavelet is important to obtain an accurate LSM image. • However, LSM images are usually better than the standard migration image.

  14. Outline • Theory • LSM resiliency to artifacts from poor acquisition geometry • LSM image sensitivity to wavelet estimation errors • Multi-scale LSM applied to poststack JAPEX data • Target-oriented 3D LSM applied to poststack PEMEX data • Conclusions

  15. Multi-scale LSM • Starts by estimating a low wavenumber reflectivity model in order to avoid getting trapped in a local minimum. • Band-pass filters, where the frequency bandwidth increases with the number of iterations, were iteratively applied to the input data.

  16. Multi-scale LSM Applied to JAPEX Data X10 5 MS LSM Image Multi-scale (MS) LSM vs. Standard LSM Convergence Curves Standard LSM Image Multi-scale LSM 3.0 0.7 0.7 Standard LSM 20Hz Depth (km) Residual 25 30 32 34 36 38 40 0.5 1.9 1.9 0 40 2.4 4.9 Iteration 2.4 4.9 X (km) X (km)

  17. LSM vs. Kirchhoff Migration LSM Image Kirchhoff Migration Image 0.7 0.7 Depth (km) Depth (km) 1.9 1.9 4.9 4.9 2.4 2.4 X (km) X (km)

  18. Resolution comparison LSM vs. Standard Migration Magnitude Spectrum of Migration Image 1 LSM Image Kirchhoff Migration Image 0.7 0.7 Magnitude Depth (km) Depth (km) 0 0 0.04 1.2 1.2 Wavenumber (1/m) 4.3 4.3 3.7 3.7 X (km) X (km)

  19. Outline • Theory • LSM resiliency to artifacts from poor acquisition geometry • LSM image sensitivity to wavelet estimation errors • Multi-scale LSM applied to poststack JAPEX data • Target-oriented LSM applied to poststack PEMEX data • Conclusions

  20. PEMEX 3D OBC Data from GOM Acquired in1990s. Since acquisition geometry is sparse, noise is dominant in the shallowpart. IL3100 Stacked Section 0 TWT (s) 4 1001 1 XL Number

  21. Subset of PEMEX Data Targeted area size: # IL = 115 lines #XL = 201 lines 0 0 3036 TWT (s) TWT (s) IL Number 3 3 3150 700 3150 3036 501 501 700 IL Number XL Number XL Number

  22. LSM vs. Kirchhoff Migration from PEMEX Data IL3100 LSM Image Kirchhoff Migration Image 0.7 0.7 Depth (m) Depth (m) 1.9 1.9 4.9 2.4 2.4 4.9 X (m) X (m)

  23. Resolution comparison LSM LSM vs. Standard Migration Magnitude Spectrum of Migration Image Kirchhoff Migration 1 LSM Image Kirchhoff Migration Image 1 Magnitude Depth (km) 0 0 650 551 0.04 2.2 XL Number Wavenumber (1/m) 650 551 XL Number

  24. TO LSM Applied for 3D Data Preliminary Result of LSM Image after 4 iterations Kirchhoff Migration Image

  25. Conclusions • Numerical results show: • LSM has a significant resilience to artifacts from poor acquisition geometries . • an accurate waveletestimate provides an accurate LSM image. • Results from JAPEX and PEMEX data show: • faster convergence rate is provided by a multi-scale migration scheme. • 2D LSM is a practical means for improving quality image. • Encouraging results for TO LSM are obtained from the 3D data subset.

  26. Future work • GOAL: 3D LSMin less than 10 iterations. • Further improvement in efficiency will be investigated.

  27. Acknowledgements • We thank PEMEX Exploration and Production for permission to use and publish its Gulf of Mexico data. • I would like to thank JOGMEC and JAPEX for supporting my study at the University of Utah. • We also thank the UTAM consortium members for supporting my work.

More Related