Data-driven machine learning approaches for precise lithofacies identification in complex geological environments
文献类型：期刊论文
| 作者 | Ali, Muhammad2,3; Zhu, Peimin2; Ma, Huolin2; Jiang, Ren4; Zhang, Hao2; Ashraf, Umar1; Hussain, Wakeel2 |
| 刊名 | GEO-SPATIAL INFORMATION SCIENCE
 |
| 出版日期 | 2024-10-17 |
| 页码 | 21 |
| 关键词 | Reservoir characterization lithofacies identification machine learning core sample availability truncated Gaussian simulation |
| ISSN号 | 1009-5020 |
| DOI | 10.1080/10095020.2024.2405635 |
| 英文摘要 | Reservoir characterization is a vital task within the oil and gas industry, with the identification of lithofacies in subsurface formations being a fundamental aspect of this process. However, lithofacies identification in complex geological environments with high dimensions, such as the Lower Indus Basin in Pakistan, poses a notable challenge, especially when dealing with limited data. To address this issue, we propose four common data-driven machine learning approaches: multi-resolution graph-based clustering (MRGC), artificial neural networks (ANN), K-nearest neighbors (KNN), and self-organizing map (SOM). We utilized these proposed approaches to assess their performance in scenarios with varying core sample availability, specifically evaluating their effectiveness in identifying lithofacies within the Lower Goru formation of the middle Indus Basin. The study reveals that in scenarios with a limited number of core samples, MRGC is the preferred choice, while KNN or MRGC is more suitable for larger datasets. The results demonstrate the superior performance of MRGC and KNN in lithofacies identification within the specified geological environment, with SOM following closely behind, and ANN exhibiting comparatively lower efficacy. The accurate identification of lithofacies from the selected model is complemented by the application of the truncated Gaussian simulation method for facies modeling. Comparative results confirm the excellent agreement between the model identification of lithofacies from well logs and electro-facies obtained from the truncated Gaussian simulation electro-facies volume. This study highlights the crucial role of selecting the right machine learning approach for precise lithofacies identification and modeling in complex geological environments. The comparative analysis provides practitioners in the petroleum industry with insights into the strengths and limitations of each method, enhancing existing knowledge. In conclusion, this research emphasizes the significance of comprehensive research and method selection for advancing lithofacies identification in diverse formations or study areas, ultimately benefiting the broader field of subsurface characterization in the petroleum industry. |
| 资助项目 | National Natural Science Foundation of China[41774145] ; National Natural Science Foundation of China[72243011] ; China's National Key RD Program[2023YFB4104200] |
| WOS研究方向 | Remote Sensing |
| 语种 | 英语 |
| WOS记录号 | WOS:001335401400001 |
| 出版者 | TAYLOR & FRANCIS LTD |
| 源URL | [http://119.78.100.198/handle/2S6PX9GI/42878]  |
| 专题 | 中科院武汉岩土力学所 |
| 通讯作者 | Zhu, Peimin |
| 作者单位 | 1.Yunnan Univ, Sch Ecol & Environm Sci, Kunming, Peoples R China 2.China Univ Geosci, Inst Geophys & Geomatics, Wuhan, Peoples R China 3.Chinese Acad Sci, Inst Rock & Soil Mech, State Key Lab Geomech & Geotech Engn, Wuhan, Peoples R China 4.Petro China Co Ltd, Res Inst Petr Explorat & Dev, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Ali, Muhammad,Zhu, Peimin,Ma, Huolin,et al. Data-driven machine learning approaches for precise lithofacies identification in complex geological environments[J]. GEO-SPATIAL INFORMATION SCIENCE,2024:21. |
| APA | Ali, Muhammad.,Zhu, Peimin.,Ma, Huolin.,Jiang, Ren.,Zhang, Hao.,...&Hussain, Wakeel.(2024).Data-driven machine learning approaches for precise lithofacies identification in complex geological environments.GEO-SPATIAL INFORMATION SCIENCE,21. |
| MLA | Ali, Muhammad,et al."Data-driven machine learning approaches for precise lithofacies identification in complex geological environments".GEO-SPATIAL INFORMATION SCIENCE (2024):21. |
入库方式： OAI收割
来源：武汉岩土力学研究所
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