Enhancing reservoir characterization: A novel machine learning approach for automated detection and reconstruction of outliers-affected well log curves
文献类型：期刊论文
| 作者 | Hussain, Wakeel6,7; Luo, Miao7; Ali, Muhammad5; Kasala, Erasto E.3,4; Hussain, Irshad2; Hussain, Muzahir6; Mkono, Christopher N.1; Silingi, Selemani Ng'wendesha1 |
| 刊名 | PHYSICS OF FLUIDS
 |
| 出版日期 | 2025-03-01 |
| 卷号 | 37期号:3页码:25 |
| ISSN号 | 1070-6631 |
| DOI | 10.1063/5.0255495 |
| 英文摘要 | The drilling process can result in irregular measurements due to unconsolidated geological formations, affecting the accuracy of wireline logging devices. This impacts the precision of elastic log measurements, such as velocity and density profiles, which are essential for reservoir characterization. The reliability of the wireline-logging tool is crucial in preventing inaccuracies when assessing reservoir rock properties. Previous studies have focused on applying machine learning (ML) techniques to wireline logging, but these methods have limited applicability, particularly for outlier detection and log reconstruction. In response, this study integrates both supervised and unsupervised ML techniques to enhance the accuracy of elastic log responses in reservoir characterization. Initially, density-based spatial clustering of applications with noise was applied for outlier detection, followed by feature selection to identify correlated logs for reconstructing the density log. A random forest regression model, optimized with particle swarm optimization (PSO), was then trained using the selected features. The comparative analysis showed a significant improvement in porosity estimation from the reconstructed density log compared to core data. Specifically, the comparison between core and original bulk density porosity yielded an R-2 of 0.95 and a root mean squared error (RMSE) of 0.012. In contrast, the comparison between core and the rebuilt density log porosity resulted in an R-2 of 0.98 and an RMSE of 0.007. The integration of advanced ML techniques with PSO-optimized random forest models represents a considerable advancement in the field of reservoir characterization. This approach enhances accuracy but also saves time and reduces manual effort, highlighting considerable potential for the advancement of methods in petroleum exploration and production. |
| 资助项目 | National Natural Science Foundation of China ; Directorate General of Petroleum Concessions (DGPC) ; [41574121] |
| WOS研究方向 | Mechanics ; Physics |
| 语种 | 英语 |
| WOS记录号 | WOS:001446479800008 |
| 出版者 | AIP Publishing |
| 源URL | [http://119.78.100.198/handle/2S6PX9GI/36543]  |
| 专题 | 中科院武汉岩土力学所 |
| 通讯作者 | Luo, Miao |
| 作者单位 | 1.China Univ Geosci, Sch Earth Resources, Wuhan 430074, Peoples R China 2.Changan Univ, Sch Earth Sci & Resources, Xian 710054, Peoples R China 3.Univ Dar Es Salaam, Dept Petr Sci & Engn, POB 35091, Dar Es Salaam, Tanzania 4.China Univ Geosci, Key Lab Tecton & Petr Resources, Minist Educ, Wuhan 430074, Peoples R China 5.Chinese Acad Sci, Inst Rock & Soil Mech, State Key Lab Geomech & Geotech Engn, Wuhan 430071, Peoples R China 6.China Univ Geosci, Sch Geophys & Geomat, Wuhan 430074, Peoples R China 7.China Univ Geosci, Sch Geophys & Geomat, Hubei Subsurface Multiscale Image Key Lab, Wuhan 430074, Peoples R China |
| 推荐引用方式 GB/T 7714 | Hussain, Wakeel,Luo, Miao,Ali, Muhammad,et al. Enhancing reservoir characterization: A novel machine learning approach for automated detection and reconstruction of outliers-affected well log curves[J]. PHYSICS OF FLUIDS,2025,37(3):25. |
| APA | Hussain, Wakeel.,Luo, Miao.,Ali, Muhammad.,Kasala, Erasto E..,Hussain, Irshad.,...&Silingi, Selemani Ng'wendesha.(2025).Enhancing reservoir characterization: A novel machine learning approach for automated detection and reconstruction of outliers-affected well log curves.PHYSICS OF FLUIDS,37(3),25. |
| MLA | Hussain, Wakeel,et al."Enhancing reservoir characterization: A novel machine learning approach for automated detection and reconstruction of outliers-affected well log curves".PHYSICS OF FLUIDS 37.3(2025):25. |
入库方式： OAI收割
来源：武汉岩土力学研究所
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