Advancing Reservoir Evaluation: Machine Learning Approaches for Predicting Porosity Curves
文献类型：期刊论文
| 作者 | Ali, Nafees1,2,3,4; Fu, Xiaodong1,2,3,4; Chen, Jian1,2,3,4; Hussain, Javid1,2,3,4; Hussain, Wakeel5; Rahman, Nosheen1,2; Iqbal, Sayed Muhammad1,2; Altalbe, Ali6 |
| 刊名 | ENERGIES
 |
| 出版日期 | 2024-08-01 |
| 卷号 | 17期号:15页码:22 |
| 关键词 | porosity curve prediction ML artificial neural network fuzzy logic |
| DOI | 10.3390/en17153768 |
| 英文摘要 | Porosity assessment is a vital component for reservoir evaluation in the oil and gas sector, and with technological advancement, reliance on conventional methods has decreased. In this regard, this research aims to reduce reliance on well logging, purposing successive machine learning (ML) techniques for precise porosity measurement. So, this research examines the prediction of the porosity curves in the Sui main and Sui upper limestone reservoir, utilizing ML approaches such as an artificial neural networks (ANN) and fuzzy logic (FL). Thus, the input dataset of this research includes gamma ray (GR), neutron porosity (NPHI), density (RHOB), and sonic (DT) logs amongst five drilled wells located in the Qadirpur gas field. The ANN model was trained using the backpropagation algorithm. For the FL model, ten bins were utilized, and Gaussian-shaped membership functions were chosen for ideal correspondence with the geophysical log dataset. The closeness of fit (C-fit) values for the ANN ranged from 91% to 98%, while the FL model exhibited variability from 90% to 95% throughout the wells. In addition, a similar dataset was used to evaluate multiple linear regression (MLR) for comparative analysis. The ANN and FL models achieved robust performance as compared to MLR, with R2 values of 0.955 (FL) and 0.988 (ANN) compared to 0.94 (MLR). The outcomes indicate that FL and ANN exceed MLR in predicting the porosity curve. Moreover, the significant R2 values and lowest root mean square error (RMSE) values support the potency of these advanced approaches. This research emphasizes the authenticity of FL and ANN in predicting the porosity curve. Thus, these techniques not only enhance natural resource exploitation within the region but also hold broader potential for worldwide applications in reservoir assessment. |
| 资助项目 | Institutional Fund Projects[IFPIP:210-611-1443] ; Ministry of Education and King Abdulaziz University, DSR, Jeddah, Saudi Arabia |
| WOS研究方向 | Energy & Fuels |
| 语种 | 英语 |
| WOS记录号 | WOS:001287052600001 |
| 出版者 | MDPI |
| 源URL | [http://119.78.100.198/handle/2S6PX9GI/42244]  |
| 专题 | 中科院武汉岩土力学所 |
| 通讯作者 | Chen, Jian; Hussain, Wakeel |
| 作者单位 | 1.Chinese Acad Sci, Inst Rock & Soil Mech, State Key Lab Geomech & Geotech Engn, Wuhan 430071, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.China Pakistan Joint Res Ctr Earth Sci, Islamabad 45320, Pakistan 4.Hubei Key Lab Geoenvironm Engn, Wuhan 430071, Peoples R China 5.China Univ Geosci, Sch Geophys & Geomat, Wuhan 430079, Peoples R China 6.King Abdulaziz Univ, Fac Comp & Informat Technol, Jeddah 21589, Saudi Arabia |
| 推荐引用方式 GB/T 7714 | Ali, Nafees,Fu, Xiaodong,Chen, Jian,et al. Advancing Reservoir Evaluation: Machine Learning Approaches for Predicting Porosity Curves[J]. ENERGIES,2024,17(15):22. |
| APA | Ali, Nafees.,Fu, Xiaodong.,Chen, Jian.,Hussain, Javid.,Hussain, Wakeel.,...&Altalbe, Ali.(2024).Advancing Reservoir Evaluation: Machine Learning Approaches for Predicting Porosity Curves.ENERGIES,17(15),22. |
| MLA | Ali, Nafees,et al."Advancing Reservoir Evaluation: Machine Learning Approaches for Predicting Porosity Curves".ENERGIES 17.15(2024):22. |
入库方式： OAI收割
来源：武汉岩土力学研究所
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