Quantitative 3d Integration Of Electromagnetic And Seismic Data To Optimize Structural Similarity Inversion Results In Offshore Northwest Borneo, Malaysia

Three-dimensional (3D) inverse modelling of seismic and electromagnetic (EM) geophysical data coupled by the cross-gradient criterion is well known to maximize accuracy and reduce uncertainty in subsurface exploration but this requires solving a large-size inverse problem by regularization methods w...

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Main Author: Saleh, Ahmad Shahir
Format: Thesis
Language:English
Published: 2023
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Online Access:http://eprints.usm.my/60081/1/AHMAD%20SHAHIR%20BIN%20SALEH%20-%20TESIS%20cut.pdf
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spelling my-usm-ep.600812024-03-08T02:00:22Z Quantitative 3d Integration Of Electromagnetic And Seismic Data To Optimize Structural Similarity Inversion Results In Offshore Northwest Borneo, Malaysia 2023-07 Saleh, Ahmad Shahir QC1 Physics (General) Three-dimensional (3D) inverse modelling of seismic and electromagnetic (EM) geophysical data coupled by the cross-gradient criterion is well known to maximize accuracy and reduce uncertainty in subsurface exploration but this requires solving a large-size inverse problem by regularization methods whose optimization is not fully understood and require further research. This study investigated the optimization of 3D regularized inversion using seismic, controlled source EM (CSEM), magnetotelluric (MT) and well data from offshore northwest Borneo. The effect of regularization was studied by performing several inversions of realistic synthetic 3D MT data. Using the best regularization weight found, the optimal cross-gradient weight was found by comparing 3D MT inversion results against the available resistivity well logs for several trial weights. Interpolation of resistivity logs with seismic velocity as secondary guide was found to be the best approach for building the initial resistivity model. The optimized seismic-guided MT inversion was found to produce a more geologically plausible model as compared to the model from the conventional approach. Finally, 3D anisotropic joint CSEM-MT inversion and seismic inversion were conducted. A new attribute termed the seismic-EM projection (SEMP) was defined to further qualify potential reservoirs based on elastic properties and resistivity values. To avoid the risk of encountering tight brine reservoir, the extended elastic impedance (EEI) approach was used to predict total porosity in 3D. The SEMP and EEI attributes have highlighted several geobodies with hydrocarbon potential. 2023-07 Thesis http://eprints.usm.my/60081/ http://eprints.usm.my/60081/1/AHMAD%20SHAHIR%20BIN%20SALEH%20-%20TESIS%20cut.pdf application/pdf en public phd doctoral Universiti Sains Malaysia Pusat Pengajian Sains Fizik
institution Universiti Sains Malaysia
collection USM Institutional Repository
language English
topic QC1 Physics (General)
spellingShingle QC1 Physics (General)
Saleh, Ahmad Shahir
Quantitative 3d Integration Of Electromagnetic And Seismic Data To Optimize Structural Similarity Inversion Results In Offshore Northwest Borneo, Malaysia
description Three-dimensional (3D) inverse modelling of seismic and electromagnetic (EM) geophysical data coupled by the cross-gradient criterion is well known to maximize accuracy and reduce uncertainty in subsurface exploration but this requires solving a large-size inverse problem by regularization methods whose optimization is not fully understood and require further research. This study investigated the optimization of 3D regularized inversion using seismic, controlled source EM (CSEM), magnetotelluric (MT) and well data from offshore northwest Borneo. The effect of regularization was studied by performing several inversions of realistic synthetic 3D MT data. Using the best regularization weight found, the optimal cross-gradient weight was found by comparing 3D MT inversion results against the available resistivity well logs for several trial weights. Interpolation of resistivity logs with seismic velocity as secondary guide was found to be the best approach for building the initial resistivity model. The optimized seismic-guided MT inversion was found to produce a more geologically plausible model as compared to the model from the conventional approach. Finally, 3D anisotropic joint CSEM-MT inversion and seismic inversion were conducted. A new attribute termed the seismic-EM projection (SEMP) was defined to further qualify potential reservoirs based on elastic properties and resistivity values. To avoid the risk of encountering tight brine reservoir, the extended elastic impedance (EEI) approach was used to predict total porosity in 3D. The SEMP and EEI attributes have highlighted several geobodies with hydrocarbon potential.
format Thesis
qualification_name Doctor of Philosophy (PhD.)
qualification_level Doctorate
author Saleh, Ahmad Shahir
author_facet Saleh, Ahmad Shahir
author_sort Saleh, Ahmad Shahir
title Quantitative 3d Integration Of Electromagnetic And Seismic Data To Optimize Structural Similarity Inversion Results In Offshore Northwest Borneo, Malaysia
title_short Quantitative 3d Integration Of Electromagnetic And Seismic Data To Optimize Structural Similarity Inversion Results In Offshore Northwest Borneo, Malaysia
title_full Quantitative 3d Integration Of Electromagnetic And Seismic Data To Optimize Structural Similarity Inversion Results In Offshore Northwest Borneo, Malaysia
title_fullStr Quantitative 3d Integration Of Electromagnetic And Seismic Data To Optimize Structural Similarity Inversion Results In Offshore Northwest Borneo, Malaysia
title_full_unstemmed Quantitative 3d Integration Of Electromagnetic And Seismic Data To Optimize Structural Similarity Inversion Results In Offshore Northwest Borneo, Malaysia
title_sort quantitative 3d integration of electromagnetic and seismic data to optimize structural similarity inversion results in offshore northwest borneo, malaysia
granting_institution Universiti Sains Malaysia
granting_department Pusat Pengajian Sains Fizik
publishDate 2023
url http://eprints.usm.my/60081/1/AHMAD%20SHAHIR%20BIN%20SALEH%20-%20TESIS%20cut.pdf
_version_ 1794024088093589504