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==Wave-equation migration Q analysis == | ==Wave-equation migration Q analysis == | ||

- | // by Yi Shen // | + | // by [[ http://sepwww.stanford.edu/data/media/public/sep/yishen/Site/Welcome.html|Yi Shen]]// |

**Downloads** | **Downloads** | ||

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**Table of contents** | **Table of contents** | ||

- | * Part I: Seismic time-lapse analysis | + | * Chapter 1: Introduction |

- | * Chapter 1: Introduction and overview of Part I | + | * Chapter 2: Wave-equation migration Q analysis |

- | * Chapter 2: Time-lapse scattering theory | + | * Chapter 3: Rock physics constrained WEMQA |

- | * Chapter 3: Simultaneous time-lapse full-waveform inversion | + | * Chapter 4: Multi-parameter inversion of velocity and Q using wave-equation migration analysis |

- | * Chapter 4: Sensitivity analysis of simultaneous phase-only reflection time-lapse FWI | + | * Chapter 5: Field data application |

- | * Chapter 5: Case study: Gulf of Mexico Genesis Field | + | * Chapter 6: Conclusions |

- | * Part II: Geomechanical time-lapse analysis | + | * Appendix A: Spectral ratio method for migrated events |

- | * Chapter 6: Relating surface deformation to pressure change | + | * Appendix B: Image perturbation |

- | * Chapter 7: Reservoir monitoring by inverting pore pressure changes from surface deformation | + | * Appendix C: Wave-equation Q tomographic operator |

- | * Chapter 8: Characterization of reservoir heterogeneity | + | |

- | * Part III: Appendices | + | |

- | * Appendix A: Yet another guide to computing FWI objective functional | + | |

- | * Appendix B: Compressive Conjugate Directions: Linear Theory | + | |

- | * Appendix C: Total-variation minimization with bound constraints | + | |

- | * Appendix D: Useful functions and equations | + | |

* Bibliography | * Bibliography | ||

- | * Index\\ | + | |

**Abstract**\\ | **Abstract**\\ | ||

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Such developed methods require highly accurate velocity models. Therefore, I also develop a multi-parameter inversion of velocity and Q models using wave-equation migration analysis. This method poses the estimation problem as an optimization problem that seeks optimum velocity and Q models by minimizing user-defined image residuals. The numerical tests on a modified SEAM model with two gas clouds demonstrate the benefit of using such multi-parameter inversion, when the existing velocity and Q models are inaccurate. The results show that this inversion method is able to retrieve both velocity and Q models, and to correct and compensate the distorted migrated image caused by inaccurate velocity and Q models. I apply this joint inversion of velocity and Q models to the 3D Dolphin’s multi-client field data acquired in the North Sea, which have attenuation and velocity problems due to shallow subsurface gas chimneys and channels that are correlated with strong attenuation and low-interval velocity. The updated velocity shows low velocity regions around the gas and channel features. The inverted Q model detects the shape and location of the gas and channel areas, which align with Dolphin’s interpretation. Consequently, the migration with the updated velocity model and the estimated Q anomalies flattens the events in the subsurface angle gathers, enhances the damped amplitudes and the frequency content of the migrated events, corrects the distorted phase of the migrated events and makes them more coherent. | Such developed methods require highly accurate velocity models. Therefore, I also develop a multi-parameter inversion of velocity and Q models using wave-equation migration analysis. This method poses the estimation problem as an optimization problem that seeks optimum velocity and Q models by minimizing user-defined image residuals. The numerical tests on a modified SEAM model with two gas clouds demonstrate the benefit of using such multi-parameter inversion, when the existing velocity and Q models are inaccurate. The results show that this inversion method is able to retrieve both velocity and Q models, and to correct and compensate the distorted migrated image caused by inaccurate velocity and Q models. I apply this joint inversion of velocity and Q models to the 3D Dolphin’s multi-client field data acquired in the North Sea, which have attenuation and velocity problems due to shallow subsurface gas chimneys and channels that are correlated with strong attenuation and low-interval velocity. The updated velocity shows low velocity regions around the gas and channel features. The inverted Q model detects the shape and location of the gas and channel areas, which align with Dolphin’s interpretation. Consequently, the migration with the updated velocity model and the estimated Q anomalies flattens the events in the subsurface angle gathers, enhances the damped amplitudes and the frequency content of the migrated events, corrects the distorted phase of the migrated events and makes them more coherent. | ||

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+ | **Reproducibility and source codes**\\ | ||

+ | This thesis has been tested for [[sep:research:reproducible|reproducibility]]. The source codes are made available for [[http://sepwww.stanford.edu/data/media/private/docs/sep166/source/source.tar.gz|download]]. The scripts for field data applications are available for [[http://sepwww.stanford.edu/data/media/private/docs/sep166/source/shScript.tar.gz|download]].\\ | ||

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+ | ** Programs for Q migration compensation and Q tomography**\\ | ||

+ | Download [[http://sepwww.stanford.edu/data/media/private/docs/sep166/source/bin.tar.gz|here]]. \\ | ||

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+ | **Defense**\\ | ||

+ | [[http://sepwww.stanford.edu/data/media/public/docs/sep166/yi_defense_clean.pdf|Defense presentation]]\\ | ||

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