Institutional Repository of Key Laboratory of Petroleum Resource Research, Chinese Academy of Sciences
| Lithofacies logging identification for strongly heterogeneous deep-buried reservoirs based on improved Bayesian inversion: The Lower Jurassic sandstone, Central Junggar Basin, China | |
| Zheng, Zongyuan1,2,3; Zhang, Likuan1,2; Cheng, Ming1,2; Lei, Yuhong1,2; Zhang, Zengbao4; Zeng, Zhiping4; Ren, Xincheng4; Yu, Lan5; Yang, Wenxiu6; Li, Chao1,2; Liu, Naigui1,2 | |
| 2023-01-20 | |
| Source Publication | FRONTIERS IN EARTH SCIENCE
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| Volume | 11Pages:18 |
| Abstract | The strong heterogeneity characteristics of deep-buried clastic low-permeability reservoirs may lead to great risks in hydrocarbon exploration and development, which makes the accurate identification of reservoir lithofacies crucial for improving the obtained exploration results. Due to the very limited core data acquired from deep drilling, lithofacies logging identification has become the most important method for comprehensively obtaining the rock information of deep-buried reservoirs and is a fundamental task for carrying out reservoir characterization and geological modeling. In this study, a machine learning method is introduced to lithofacies logging identification, to explore an accurate lithofacies identification method for deep fluvial-delta sandstone reservoirs with frequent lithofacies changes. Here Sangonghe Formation in the Central Junggar Basin of China is taken as an example. The K-means-based synthetic minority oversampling technique (K-means SMOTE) is employed to solve the problem regarding the imbalanced lithofacies data categories used to calibrate logging data, and a probabilistic calibration method is introduced to correct the likelihood function. To address the situation in which traditional machine learning methods ignore the geological deposition process, we introduce a depositional prior for controlling the vertical spreading process based on a Markov chain and propose an improved Bayesian inversion process for training on the log data to identify lithofacies. The results of a series of experiments show that, compared with the traditional machine learning method, the new method improves the recognition accuracy by 20%, and the predicted petrographic vertical distribution results are consistent with geological constraints. In addition, SMOTE and probabilistic calibration can effectively handle data imbalance problems so that different categories can be adequately learned. Also the introduction of geological prior has a positive impact on the overall distribution, which significantly improves the accuracy and recall rate of the method. According to this comprehensive analysis, the proposed method greatly enhanced the identification of the lithofacies distributions in the Sangonghe Formation. Therefore, this method can provide a tool for logging lithofacies interpretation of deep and strongly heterogeneous clastic reservoirs in fluvial-delta and other depositional environments. |
| Keyword | logging identification machine learning Bayesian inversion Junggar Basin Sangonghe Formation reservoir lithofacies |
| DOI | 10.3389/feart.2023.1095611 |
| Funding Organization | National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences |
| WOS Keyword | LITHOLOGY PREDICTION ; MEMBER ; MODEL ; AREA |
| Language | 英语 |
| Funding Project | National Natural Science Foundation of China[42030808] ; National Natural Science Foundation of China[42102176] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA14010202] |
| Funding Organization | National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences |
| WOS Research Area | Geology |
| WOS Subject | Geosciences, Multidisciplinary |
| WOS ID | WOS:000925501400001 |
| Publisher | FRONTIERS MEDIA SA |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.iggcas.ac.cn/handle/132A11/106797 |
| Collection | 油气资源研究院重点实验室 |
| Corresponding Author | Zhang, Likuan |
| Affiliation | 1.Chinese Acad Sci, Inst Geol & Geophys, Key Lab Petr Resources Res, Beijing, Peoples R China 2.Chinese Acad Sci, Innovat Acad Earth Sci, Beijing, Peoples R China 3.Univ Chinese Acad Sci, Beijing, Peoples R China 4.Sinopec Shengli Oilfield Co, Dongying, Shandong, Peoples R China 5.Sinopec Petr Explorat & Prod Res Inst, Beijing, Peoples R China 6.AspenTech Subsurface Sci & Engn, Lysaker, Norway |
| First Author Affilication | Institute of Geology and Geophysics, Chinese Academy of Sciences |
| Corresponding Author Affilication | Institute of Geology and Geophysics, Chinese Academy of Sciences |
| Recommended Citation GB/T 7714 | Zheng, Zongyuan,Zhang, Likuan,Cheng, Ming,et al. Lithofacies logging identification for strongly heterogeneous deep-buried reservoirs based on improved Bayesian inversion: The Lower Jurassic sandstone, Central Junggar Basin, China[J]. FRONTIERS IN EARTH SCIENCE,2023,11:18. |
| APA | Zheng, Zongyuan.,Zhang, Likuan.,Cheng, Ming.,Lei, Yuhong.,Zhang, Zengbao.,...&Liu, Naigui.(2023).Lithofacies logging identification for strongly heterogeneous deep-buried reservoirs based on improved Bayesian inversion: The Lower Jurassic sandstone, Central Junggar Basin, China.FRONTIERS IN EARTH SCIENCE,11,18. |
| MLA | Zheng, Zongyuan,et al."Lithofacies logging identification for strongly heterogeneous deep-buried reservoirs based on improved Bayesian inversion: The Lower Jurassic sandstone, Central Junggar Basin, China".FRONTIERS IN EARTH SCIENCE 11(2023):18. |
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