Prediction of marl reservoir distribution based on facies-constrained reflectivity inversion method
Accurate characterization of carbonate reservoirs remains a significant challenge due to complex facies variations and the substantial effects of wave propagation. We propose a facies-constrained reflectivity inversion strategy. The method establishes a relationship between logging data and seismic...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-12-01
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| Series: | Frontiers in Earth Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/feart.2024.1495720/full |
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| author | Hao Zhang Li Chen Hua Zhu Yongguang Xin Yongxiao Wang Xiaowei Sun |
| author_facet | Hao Zhang Li Chen Hua Zhu Yongguang Xin Yongxiao Wang Xiaowei Sun |
| author_sort | Hao Zhang |
| collection | DOAJ |
| description | Accurate characterization of carbonate reservoirs remains a significant challenge due to complex facies variations and the substantial effects of wave propagation. We propose a facies-constrained reflectivity inversion strategy. The method establishes a relationship between logging data and seismic waveforms, applies clustering analysis using the Self-Organizing Map (SOM) technique, and utilizes the clustering results to constrain the construction of an initial model with realistic lateral variations. Based on this initial model, a Bayesian-based reflectivity inversion is performed, incorporating a modified Cauchy prior distribution to enhance inversion accuracy and stability. The reflectivity method offers a one-dimensional analytical solution to the wave equation, tacking thin layer thicknesses and wave propagation effects into consideration, thereby significantly alleviating inversion problems encountered in marl reservoirs. Compared to traditional inversion methods based on the Zoeppritz equation, the facies-constrained reflectivity inversion delivers higher accuracy and resolution. The application of this technique to identify marl reservoirs in the Lei32 sub-member of the Sichuan Basin has yielded promising results, effectively delineating favorable reservoir areas of approximately 210 km2 and offering strong support for future exploration and development. |
| format | Article |
| id | doaj-art-8c7161573a4a4f0db4ae6f5b95d631b2 |
| institution | Kabale University |
| issn | 2296-6463 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Earth Science |
| spelling | doaj-art-8c7161573a4a4f0db4ae6f5b95d631b22024-12-06T06:50:51ZengFrontiers Media S.A.Frontiers in Earth Science2296-64632024-12-011210.3389/feart.2024.14957201495720Prediction of marl reservoir distribution based on facies-constrained reflectivity inversion methodHao Zhang0Li Chen1Hua Zhu2Yongguang Xin3Yongxiao Wang4Xiaowei Sun5Petrochina Hangzhou Research Institute of Geology, Hangzhou, ChinaCollege of Geophysics, Chengdu University of Technology, Chengdu, ChinaPetroChina Southwest Oil and Gasfield Company, Chengdu, ChinaPetrochina Hangzhou Research Institute of Geology, Hangzhou, ChinaPetrochina Hangzhou Research Institute of Geology, Hangzhou, ChinaPetrochina Hangzhou Research Institute of Geology, Hangzhou, ChinaAccurate characterization of carbonate reservoirs remains a significant challenge due to complex facies variations and the substantial effects of wave propagation. We propose a facies-constrained reflectivity inversion strategy. The method establishes a relationship between logging data and seismic waveforms, applies clustering analysis using the Self-Organizing Map (SOM) technique, and utilizes the clustering results to constrain the construction of an initial model with realistic lateral variations. Based on this initial model, a Bayesian-based reflectivity inversion is performed, incorporating a modified Cauchy prior distribution to enhance inversion accuracy and stability. The reflectivity method offers a one-dimensional analytical solution to the wave equation, tacking thin layer thicknesses and wave propagation effects into consideration, thereby significantly alleviating inversion problems encountered in marl reservoirs. Compared to traditional inversion methods based on the Zoeppritz equation, the facies-constrained reflectivity inversion delivers higher accuracy and resolution. The application of this technique to identify marl reservoirs in the Lei32 sub-member of the Sichuan Basin has yielded promising results, effectively delineating favorable reservoir areas of approximately 210 km2 and offering strong support for future exploration and development.https://www.frontiersin.org/articles/10.3389/feart.2024.1495720/fullcarbonate reservoirsfacies-constrained inversionclustering analysisreflectivity methodbayesian |
| spellingShingle | Hao Zhang Li Chen Hua Zhu Yongguang Xin Yongxiao Wang Xiaowei Sun Prediction of marl reservoir distribution based on facies-constrained reflectivity inversion method Frontiers in Earth Science carbonate reservoirs facies-constrained inversion clustering analysis reflectivity method bayesian |
| title | Prediction of marl reservoir distribution based on facies-constrained reflectivity inversion method |
| title_full | Prediction of marl reservoir distribution based on facies-constrained reflectivity inversion method |
| title_fullStr | Prediction of marl reservoir distribution based on facies-constrained reflectivity inversion method |
| title_full_unstemmed | Prediction of marl reservoir distribution based on facies-constrained reflectivity inversion method |
| title_short | Prediction of marl reservoir distribution based on facies-constrained reflectivity inversion method |
| title_sort | prediction of marl reservoir distribution based on facies constrained reflectivity inversion method |
| topic | carbonate reservoirs facies-constrained inversion clustering analysis reflectivity method bayesian |
| url | https://www.frontiersin.org/articles/10.3389/feart.2024.1495720/full |
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