Evaluation of Reservoir Porosity and Permeability from Well Log Data Based on an Ensemble Approach: A Comprehensive Study Incorporating Experimental, Simulation, and Fieldwork Data
文献类型：期刊论文
| 作者 | Nyakilla, Edwin E.2,3; Guanhua, Sun2,3; Hongliang, Hao3; Charles, Grant4; Nafouanti, Mouigni B.4; Ricky, Emanuel X.4; Silingi, Selemani N.4,5; Abelly, Elieneza N.4; Shanghvi, Eric R.4; Naqibulla, Safi4 |
| 刊名 | NATURAL RESOURCES RESEARCH
 |
| 出版日期 | 2024-09-18 |
| 页码 | 26 |
| 关键词 | Machine learning AdaBoost multivariate analysis permeability porosity well logs |
| ISSN号 | 1520-7439 |
| DOI | 10.1007/s11053-024-10402-9 |
| 英文摘要 | Permeability and porosity are key parameters in reservoir characterization for understanding hydrocarbon flow behavior. While traditional laboratory core analysis is time-consuming, machine learning has emerged as a valuable tool for more efficient and accurate estimation. This paper proposes an ensemble technique called adaptive boosting (AdaBoost) for porosity and permeability estimation, utilizing methods such as support vector machine (SVM), Gaussian process regression (GPR), multivariate analysis, and backpropagation neural network (BPNN) for prediction based on well logs. Performance evaluation metrics including root mean square error, mean square error, and coefficient of determination (R2) were used to compare the models. The results demonstrate that AdaBoost outperformed GPR, SVM, and BPNN models in terms of processing time and accuracy, achieving R2 values of 0.980 and 0.962 for permeability and porosity during training, respectively, and 0.960 and 0.951 during testing, respectively. This study highlights AdaBoost as a robust and accurate technique that can enhance reservoir characterization. |
| 资助项目 | Innovative Research Group Project of the National Natural Science Foundation of China[2022CFD031] ; Natural Science Foundation of Hubei[12302507] ; National Science Foundation for Young Scientists of China |
| WOS研究方向 | Geology |
| 语种 | 英语 |
| WOS记录号 | WOS:001315025500001 |
| 出版者 | SPRINGER |
| 源URL | [http://119.78.100.198/handle/2S6PX9GI/42576]  |
| 专题 | 中科院武汉岩土力学所 |
| 通讯作者 | Nyakilla, Edwin E.; Guanhua, Sun; Hongliang, Hao |
| 作者单位 | 1.Mbeya Univ Sci & Technol MUST, POB 131, Mbeya, Tanzania 2.Chinese Acad Sci, Inst Rock & Soil Mech, State Key Lab Geomech & Geotech Engn, Wuhan 430071, Peoples R China 3.Peking Univ, Ordos Res Inst Energy, Huineng Kechuang Bldg,Minzu Rd, Ordos 017010, Inner Mongolia, Peoples R China 4.China Univ Geosci, Dept Petr Geol, Sch Earth Resources, Wuhan 430074, Peoples R China 5.Earth Sci Inst Shinyanga ESIS, Dept Geol, POB 1016, Shinyanga, Tanzania 6.China Univ Petr, Coll Petr Engn, Beijing 102249, Peoples R China 7.China Univ Petr East China, Sch Petr Engn, Qingdao 266580, Peoples R China |
| 推荐引用方式 GB/T 7714 | Nyakilla, Edwin E.,Guanhua, Sun,Hongliang, Hao,et al. Evaluation of Reservoir Porosity and Permeability from Well Log Data Based on an Ensemble Approach: A Comprehensive Study Incorporating Experimental, Simulation, and Fieldwork Data[J]. NATURAL RESOURCES RESEARCH,2024:26. |
| APA | Nyakilla, Edwin E..,Guanhua, Sun.,Hongliang, Hao.,Charles, Grant.,Nafouanti, Mouigni B..,...&Dan, Li.(2024).Evaluation of Reservoir Porosity and Permeability from Well Log Data Based on an Ensemble Approach: A Comprehensive Study Incorporating Experimental, Simulation, and Fieldwork Data.NATURAL RESOURCES RESEARCH,26. |
| MLA | Nyakilla, Edwin E.,et al."Evaluation of Reservoir Porosity and Permeability from Well Log Data Based on an Ensemble Approach: A Comprehensive Study Incorporating Experimental, Simulation, and Fieldwork Data".NATURAL RESOURCES RESEARCH (2024):26. |
入库方式： OAI收割
来源：武汉岩土力学研究所
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