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Advanced Least Squares Migration for Improved Image Quality in Exploration Data

Explore the theory and applications of Least Squares Migration (LSM) techniques on poststack data from JAPEX & PEMEX. Learn about the resiliency to artifacts and sensitivity to wavelet errors. See how LSM enhances image quality through multi-scale and target-oriented approaches, leading to valuable insights.

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Advanced Least Squares Migration for Improved Image Quality in Exploration Data

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  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. 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.

  8. 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

  9. 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).

  10. 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)

  11. 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)

  12. 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.

  13. 2D Poststack Data from Japan Sea JAPEX 2D SSP marine data description: Acquired in 1974, Dominant frequency of 15 Hz. 0 TWT (s) 5 0 20 X (km)

  14. 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.

  15. 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 1.9 0.5 1.9 0 40 2.4 4.9 Iteration 2.4 4.9 X (km) X (km)

  16. 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)

  17. 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)

  18. 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

  19. 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

  20. 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)

  21. 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

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

  23. 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 obtained from the 3D data subset.

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

  25. 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.

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