Reservoir Quality Prediction of Gas-Bearing Carbonate Sediments in the Qadirpur Field: Insights from Advanced Machine Learning Approaches of SOM and Cluster Analysis
文献类型：期刊论文
| 作者 | Rashid, Muhammad1; Luo, Miao1,10; Ashraf, Umar2,10; Hussain, Wakeel1,3; Ali, Nafees4,5; Rahman, Nosheen6; Hussain, Sartaj1; Martyushev, Dmitriy Aleksandrovich7; Thanh, Hung Vo8,9; Anees, Aqsa2 |
| 刊名 | MINERALS
 |
| 出版日期 | 2023 |
| 卷号 | 13期号:1页码:- |
| 关键词 | reservoir quality prediction machine learning SOM lithological identification cluster analysis |
| 英文摘要 | The detailed reservoir characterization was examined for the Central Indus Basin (CIB), Pakistan, across Qadirpur Field Eocene rock units. Various petrophysical parameters were analyzed with the integration of various cross-plots, complex water saturation, shale volume, effective porosity, total porosity, hydrocarbon saturation, neutron porosity and sonic concepts, gas effects, and lithology. In total, 8-14% of high effective porosity and 45-62% of hydrocarbon saturation are superbly found in the reservoirs of the Eocene. The Sui Upper Limestone is one of the poorest reservoirs among all these reservoirs. However, this reservoir has few intervals of rich hydrocarbons with highly effective porosity values. The shale volume ranges from 30 to 43%. The reservoir is filled with effective and total porosities along with secondary porosities. Fracture-vuggy, chalky, and intracrystalline reservoirs are the main contributors of porosity. The reservoirs produce hydrocarbon without water and gas-emitting carbonates with an irreducible water saturation rate of 38-55%. In order to evaluate lithotypes, including axial changes in reservoir characterization, self-organizing maps, isoparametersetric maps of the petrophysical parameters, and litho-saturation cross-plots were constructed. Estimating the petrophysical parameters of gas wells and understanding reservoir prospects were both feasible with the methods employed in this study, and could be applied in the Central Indus Basin and anywhere else with comparable basins. |
| 学科主题 | Geochemistry & Geophysics ; Mineralogy ; Mining & Mineral Processing |
| 语种 | 英语 |
| WOS记录号 | WOS:000927161700001 |
| 出版者 | MDPI |
| 源URL | [http://119.78.100.198/handle/2S6PX9GI/35552]  |
| 专题 | 中科院武汉岩土力学所 |
| 作者单位 | 1.Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China 2.Institute for Ecological Research and Pollution Control of Plateau Lakes, School of Ecology and Environmental Science, Yunnan University, Kunming 650500, China 3.School of Earth Resources, China University of Geosciences, Wuhan 430074, China 4.State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan 430071, China 5.University of Chinese Academy of Sciences, Beijing 100049, China 6.School of Earth Resources, Department of Minerology, Petrology and Ore Deposits, China University of Geosciences, Wuhan 430074, China 7.Department of Oil and Gas Technologies, Perm National Research Polytechnic University, 614990 Perm, Russia 8.Laboratory for Computational Mechanics, Institute for Computational Science and Artificial Intelligence, Van Lang University, Ho Chi Minh City 700000, Vietnam 9.Faculty of Mechanical-Electrical and Computer Engineering, Van Lang University, Ho Chi Minh City 700000, Vietnam 10.Correspondence: luomiao@cug.edu.cn (M.L.); umarashraf@ynu.edu.cn (U.A.) |
| 推荐引用方式 GB/T 7714 | Rashid, Muhammad,Luo, Miao,Ashraf, Umar,et al. Reservoir Quality Prediction of Gas-Bearing Carbonate Sediments in the Qadirpur Field: Insights from Advanced Machine Learning Approaches of SOM and Cluster Analysis[J]. MINERALS,2023,13(1):-. |
| APA | Rashid, Muhammad.,Luo, Miao.,Ashraf, Umar.,Hussain, Wakeel.,Ali, Nafees.,...&Anees, Aqsa.(2023).Reservoir Quality Prediction of Gas-Bearing Carbonate Sediments in the Qadirpur Field: Insights from Advanced Machine Learning Approaches of SOM and Cluster Analysis.MINERALS,13(1),-. |
| MLA | Rashid, Muhammad,et al."Reservoir Quality Prediction of Gas-Bearing Carbonate Sediments in the Qadirpur Field: Insights from Advanced Machine Learning Approaches of SOM and Cluster Analysis".MINERALS 13.1(2023):-. |
入库方式： OAI收割
来源：武汉岩土力学研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。