Advanced Permeability Prediction Through Two-Dimensional Geological Feature Image Extraction with CNN Regression from Well Logs Data
文献类型：期刊论文
| 作者 | Hussain, Wakeel4,5; Luo, Miao5; Ali, Muhammad3; Rizvi, Syed Naheel Raza2; Al-Khafaji, Harith F.1; Ali, Nafees3; Ahmed, Salah Alshareef Alkfakey4 |
| 刊名 | MATHEMATICAL GEOSCIENCES
 |
| 出版日期 | 2025-01-14 |
| 页码 | 46 |
| 关键词 | Machine learning Convolutional neural networks Geophysical logs 2D geological feature image Permeability |
| ISSN号 | 1874-8961 |
| DOI | 10.1007/s11004-024-10171-4 |
| 英文摘要 | The evaluation of permeability plays an essential role in understanding subsurface fluid behavior, optimizing hydrocarbon recovery, managing reservoir performance, and facilitating the sequestration of carbon dioxide. Conventional methods for its computation, which depend on well tests and core samples, are time-consuming, costly, and limited. There is a need for more efficient and adaptable approaches that better support decision-making in the petroleum industry. Machine learning, particularly convolutional neural networks (CNNs), provides a quick and cost-effective solution for permeability prediction. This study builds a CNN regression model to predict permeability in the Sawan gas field in Pakistan, a prospective field for hydrocarbon storage and enhanced oil recovery. The geological feature image, derived from geophysical logs, includes variables like spectral gamma-ray log (SGR), density log (RHOB), neutron porosity log (NPHI), volume of shale (VSH), and effective porosity (PHIE). The significant contribution of this research is in illustrating how the integration of CNNs with geological images leads to a more accurate and efficient approach for predicting permeability, enhancing the performance of conventional neural network models. The model demonstrates exceptional efficiency, with a processing time of just 1.14 s. The training performance metrics reveal R-squared values of 0.9821 and adjusted R-squared values of 0.9818, indicating strong predictive ability. The root mean square error (RMSE) is recorded at 0.0288, the mean squared error (MSE) at 0.0008, and the mean absolute error (MAE) at 0.0213. In the cross-validation of the entire dataset, the CNN model maintains a high R-squared of 0.9812 and adjusted R-squared of 0.9810, with RMSE at 0.0507, MSE at 0.0026, and MAE at 0.0345. Furthermore, the subset testing results show that the CNN model achieves an R-squared value of 0.9869 and adjusted R-squared of 0.9865, with RMSE at 0.0297, MSE at 0.0009, and MAE at 0.0249. These results demonstrate a notable improvement over other methodologies, such as GMDH (group method of data handling)-based modified Levenberg-Marquardt (LM) and backpropagation neural networks (BPNN), confirming the CNN's enhanced performance in permeability prediction. Through the successful application of CNNs for permeability prediction, this study not only enhances the methodological framework in reservoir characterization but also enhances decision-making processes related to subsurface resource management in the petroleum industry. The implications of our findings extend to various essential areas, including hydrocarbon management and carbon dioxide sequestration, thereby enriching the existing body of knowledge in reservoir studies. |
| 资助项目 | Directorate General of Petroleum Concessions (DGPC) ; National Natural Science Foundation of China[41574121] |
| WOS研究方向 | Geology ; Mathematics |
| 语种 | 英语 |
| WOS记录号 | WOS:001395759200001 |
| 出版者 | SPRINGER HEIDELBERG |
| 源URL | [http://119.78.100.198/handle/2S6PX9GI/37815]  |
| 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.Univ Glasgow, James Watt Sch Engn, Glasgow City G12 8QQ, England 3.Chinese Acad Sci, Inst Rock & Soil Mech, State Key Lab Geomech & Geotech Engn, Wuhan 430071, Peoples R China 4.China Univ Geosci, Sch Geophys & Geomat, Wuhan, Peoples R China 5.China Univ Geosci, Sch Geophys & Geomat, Hubei Subsurface Multiscale Image Key Lab, Wuhan, Peoples R China |
| 推荐引用方式 GB/T 7714 | Hussain, Wakeel,Luo, Miao,Ali, Muhammad,et al. Advanced Permeability Prediction Through Two-Dimensional Geological Feature Image Extraction with CNN Regression from Well Logs Data[J]. MATHEMATICAL GEOSCIENCES,2025:46. |
| APA | Hussain, Wakeel.,Luo, Miao.,Ali, Muhammad.,Rizvi, Syed Naheel Raza.,Al-Khafaji, Harith F..,...&Ahmed, Salah Alshareef Alkfakey.(2025).Advanced Permeability Prediction Through Two-Dimensional Geological Feature Image Extraction with CNN Regression from Well Logs Data.MATHEMATICAL GEOSCIENCES,46. |
| MLA | Hussain, Wakeel,et al."Advanced Permeability Prediction Through Two-Dimensional Geological Feature Image Extraction with CNN Regression from Well Logs Data".MATHEMATICAL GEOSCIENCES (2025):46. |
入库方式： OAI收割
来源：武汉岩土力学研究所
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