Prestack Seismic Inversion via Nonconvex L<sub>1-2</sub> Regularization
Using seismic data, logging information, geological interpretation data, and petrophysical data, it is possible to estimate the stratigraphic texture and elastic parameters of a study area via a seismic inversion. As such, a seismic inversion is an indispensable tool in the field of oil and gas expl...
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MDPI AG
2021-12-01
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| author | Wenliang Nie Fei Xiang Bo Li Xiaotao Wen Xiangfei Nie |
| author_facet | Wenliang Nie Fei Xiang Bo Li Xiaotao Wen Xiangfei Nie |
| author_sort | Wenliang Nie |
| collection | DOAJ |
| description | Using seismic data, logging information, geological interpretation data, and petrophysical data, it is possible to estimate the stratigraphic texture and elastic parameters of a study area via a seismic inversion. As such, a seismic inversion is an indispensable tool in the field of oil and gas exploration and development. However, due to unknown natural factors, seismic inversions are often ill-conditioned problems. One way to work around this unknowable information is to determine the solution to the seismic inversion using regularization methods after adding further a priori constraints. In this study, the nonconvex L<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow></mrow><mrow><mn>1</mn><mo>−</mo><mn>2</mn></mrow></msub></semantics></math></inline-formula> regularization method is innovatively applied to the three-parameter prestack amplitude variation angle (AVA) inversion. A forward model is first derived based on the Fatti approximate formula and then low-frequency models for P impedance, S impedance, and density are established using logging and horizon data. In the Bayesian inversion framework, we derive the objective function of the prestack AVA inversion. To further improve the accuracy and stability of the inversion results, we remove the correlations between the elastic parameters that act as initial constraints in the inversion. Then, the objective function is solved by the nonconvex L<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow></mrow><mrow><mn>1</mn><mo>−</mo><mn>2</mn></mrow></msub></semantics></math></inline-formula> regularization method. Finally, we validate our inversion method by applying it to synthetic and observational data sets. The results show that our nonconvex L<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow></mrow><mrow><mn>1</mn><mo>−</mo><mn>2</mn></mrow></msub></semantics></math></inline-formula> regularization seismic inversion method yields results that are highly accurate, laterally continuous, and can be used to identify and locate reservoir formation boundaries. Overall, our method will be a useful tool in future work focused on predicting the location of reservoirs. |
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| spelling | doaj.art-ee25d8008f4647e78d6db060c7f57ea72023-11-23T03:42:04ZengMDPI AGApplied Sciences2076-34172021-12-0111241201510.3390/app112412015Prestack Seismic Inversion via Nonconvex L<sub>1-2</sub> RegularizationWenliang Nie0Fei Xiang1Bo Li2Xiaotao Wen3Xiangfei Nie4School of Electronic and Information Engineering, Chongqing Three Gorges University, Chongqing 404000, ChinaSchool of Electronic and Information Engineering, Chongqing Three Gorges University, Chongqing 404000, ChinaState Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Chengdu University of Technology, Chengdu 610059, ChinaState Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Chengdu University of Technology, Chengdu 610059, ChinaSchool of Electronic and Information Engineering, Chongqing Three Gorges University, Chongqing 404000, ChinaUsing seismic data, logging information, geological interpretation data, and petrophysical data, it is possible to estimate the stratigraphic texture and elastic parameters of a study area via a seismic inversion. As such, a seismic inversion is an indispensable tool in the field of oil and gas exploration and development. However, due to unknown natural factors, seismic inversions are often ill-conditioned problems. One way to work around this unknowable information is to determine the solution to the seismic inversion using regularization methods after adding further a priori constraints. In this study, the nonconvex L<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow></mrow><mrow><mn>1</mn><mo>−</mo><mn>2</mn></mrow></msub></semantics></math></inline-formula> regularization method is innovatively applied to the three-parameter prestack amplitude variation angle (AVA) inversion. A forward model is first derived based on the Fatti approximate formula and then low-frequency models for P impedance, S impedance, and density are established using logging and horizon data. In the Bayesian inversion framework, we derive the objective function of the prestack AVA inversion. To further improve the accuracy and stability of the inversion results, we remove the correlations between the elastic parameters that act as initial constraints in the inversion. Then, the objective function is solved by the nonconvex L<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow></mrow><mrow><mn>1</mn><mo>−</mo><mn>2</mn></mrow></msub></semantics></math></inline-formula> regularization method. Finally, we validate our inversion method by applying it to synthetic and observational data sets. The results show that our nonconvex L<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow></mrow><mrow><mn>1</mn><mo>−</mo><mn>2</mn></mrow></msub></semantics></math></inline-formula> regularization seismic inversion method yields results that are highly accurate, laterally continuous, and can be used to identify and locate reservoir formation boundaries. Overall, our method will be a useful tool in future work focused on predicting the location of reservoirs.https://www.mdpi.com/2076-3417/11/24/12015seismic inversionAVA inversionBayesian inversionnonconvex L<sub>1-2</sub> regularization |
| spellingShingle | Wenliang Nie Fei Xiang Bo Li Xiaotao Wen Xiangfei Nie Prestack Seismic Inversion via Nonconvex L<sub>1-2</sub> Regularization Applied Sciences seismic inversion AVA inversion Bayesian inversion nonconvex L<sub>1-2</sub> regularization |
| title | Prestack Seismic Inversion via Nonconvex L<sub>1-2</sub> Regularization |
| title_full | Prestack Seismic Inversion via Nonconvex L<sub>1-2</sub> Regularization |
| title_fullStr | Prestack Seismic Inversion via Nonconvex L<sub>1-2</sub> Regularization |
| title_full_unstemmed | Prestack Seismic Inversion via Nonconvex L<sub>1-2</sub> Regularization |
| title_short | Prestack Seismic Inversion via Nonconvex L<sub>1-2</sub> Regularization |
| title_sort | prestack seismic inversion via nonconvex l sub 1 2 sub regularization |
| topic | seismic inversion AVA inversion Bayesian inversion nonconvex L<sub>1-2</sub> regularization |
| url | https://www.mdpi.com/2076-3417/11/24/12015 |
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