Reservoir Characterization Based on Bayesian Amplitude Versus Offset Inversion of Marine Seismic Data
Young’s modulus and Poisson’s ratio are critical parameters for reservoir characterization using marine seismic data. Conventional amplitude versus offset (AVO) inversion methods often assume a constant S-to-P wave velocity ratio to simplify the inversion, leading to significant errors, particularly...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/13/5/948 |
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| _version_ | 1849327220228620288 |
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| author | Jianhua Wang Xinpeng Pan Wenbo Sun Chao Li Ying Zheng Xiaolong Zhao |
| author_facet | Jianhua Wang Xinpeng Pan Wenbo Sun Chao Li Ying Zheng Xiaolong Zhao |
| author_sort | Jianhua Wang |
| collection | DOAJ |
| description | Young’s modulus and Poisson’s ratio are critical parameters for reservoir characterization using marine seismic data. Conventional amplitude versus offset (AVO) inversion methods often assume a constant S-to-P wave velocity ratio to simplify the inversion, leading to significant errors, particularly in heterogeneous reservoirs. To address this, we derive a novel four-term PP-wave reflection coefficient by reparameterizing Poisson’s ratio, effectively reducing the nonlinearity associated with the velocity ratio and enhancing the stability of Poisson’s ratio estimation. Building on this, we propose a Bayesian AVO inversion framework incorporating Cauchy prior and low-frequency model regularizations. The elastic parameters are estimated using a maximum a posteriori (MAP) approach by minimizing the negative log-posterior function. Numerical simulations and seismic gather data from East China demonstrate that the proposed inversion method yields more accurate estimates of Young’s modulus and Poisson’s ratio compared to conventional approaches. This improved AVO approximation offers a more reliable tool for delineating reservoir heterogeneity in complex geological settings using marine seismic data. |
| format | Article |
| id | doaj-art-253f1aaa60fa47b1a74da43f11cc5fd7 |
| institution | Kabale University |
| issn | 2077-1312 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-253f1aaa60fa47b1a74da43f11cc5fd72025-08-20T03:47:57ZengMDPI AGJournal of Marine Science and Engineering2077-13122025-05-0113594810.3390/jmse13050948Reservoir Characterization Based on Bayesian Amplitude Versus Offset Inversion of Marine Seismic DataJianhua Wang0Xinpeng Pan1Wenbo Sun2Chao Li3Ying Zheng4Xiaolong Zhao5National Engineering Research Center of Offshore Oil and Gas Exploration, Beijing 100028, ChinaSchool of Geoscience and Info-Physics, Central South University, Changsha 410083, ChinaNational Engineering Research Center of Offshore Oil and Gas Exploration, Beijing 100028, ChinaNational Engineering Research Center of Offshore Oil and Gas Exploration, Beijing 100028, ChinaNational Engineering Research Center of Offshore Oil and Gas Exploration, Beijing 100028, ChinaCNOOC (China) Limited Beijing New Energy Branch, Beijing 102200, ChinaYoung’s modulus and Poisson’s ratio are critical parameters for reservoir characterization using marine seismic data. Conventional amplitude versus offset (AVO) inversion methods often assume a constant S-to-P wave velocity ratio to simplify the inversion, leading to significant errors, particularly in heterogeneous reservoirs. To address this, we derive a novel four-term PP-wave reflection coefficient by reparameterizing Poisson’s ratio, effectively reducing the nonlinearity associated with the velocity ratio and enhancing the stability of Poisson’s ratio estimation. Building on this, we propose a Bayesian AVO inversion framework incorporating Cauchy prior and low-frequency model regularizations. The elastic parameters are estimated using a maximum a posteriori (MAP) approach by minimizing the negative log-posterior function. Numerical simulations and seismic gather data from East China demonstrate that the proposed inversion method yields more accurate estimates of Young’s modulus and Poisson’s ratio compared to conventional approaches. This improved AVO approximation offers a more reliable tool for delineating reservoir heterogeneity in complex geological settings using marine seismic data.https://www.mdpi.com/2077-1312/13/5/948Bayesian AVO inversionseismic reservoir characterizationYoung’s modulusPoisson’s ratiomarine seismic data |
| spellingShingle | Jianhua Wang Xinpeng Pan Wenbo Sun Chao Li Ying Zheng Xiaolong Zhao Reservoir Characterization Based on Bayesian Amplitude Versus Offset Inversion of Marine Seismic Data Journal of Marine Science and Engineering Bayesian AVO inversion seismic reservoir characterization Young’s modulus Poisson’s ratio marine seismic data |
| title | Reservoir Characterization Based on Bayesian Amplitude Versus Offset Inversion of Marine Seismic Data |
| title_full | Reservoir Characterization Based on Bayesian Amplitude Versus Offset Inversion of Marine Seismic Data |
| title_fullStr | Reservoir Characterization Based on Bayesian Amplitude Versus Offset Inversion of Marine Seismic Data |
| title_full_unstemmed | Reservoir Characterization Based on Bayesian Amplitude Versus Offset Inversion of Marine Seismic Data |
| title_short | Reservoir Characterization Based on Bayesian Amplitude Versus Offset Inversion of Marine Seismic Data |
| title_sort | reservoir characterization based on bayesian amplitude versus offset inversion of marine seismic data |
| topic | Bayesian AVO inversion seismic reservoir characterization Young’s modulus Poisson’s ratio marine seismic data |
| url | https://www.mdpi.com/2077-1312/13/5/948 |
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