Knowledge-based rigorous machine learning techniques to predict the deliverability of underground natural gas storage sites for contributing to sustainable development goals
文献类型：期刊论文
| 作者 | Hung Vo Thanh2,7; Safaei-Farouji, Majid3,7; Wei, Ning4,7; Band, Shahab S.5,7; Mosavi, Amir1,6,7 |
| 刊名 | ENERGY REPORTS
 |
| 出版日期 | 2022-11-01 |
| 卷号 | 8期号:-页码:7643 |
| 关键词 | Natural gas Machine learning Least squares support vector machine |
| ISSN号 | 2352-4847 |
| 英文摘要 | This study presents a method to develop a series of unique deliverability smart models for underground natural gas storage (UNGS) in different types of target formations. The natural gas supply loop is defined by periodic mismatches between demand and supply. Efficient and faster approaches for forecasting UNGS deliverability may not only assist stakeholders but also the competitive natural gas industry. Due to this fact, this article suggests a series of robust deliverability estimation models for 387 UNGS sites in depleted fields, aquifers, and salt domes based on rigorous machine learning (ML) techniques. To this end, the potential of three ML algorithms, including Gaussian Process Regression (GPR), Least Squares Support Vector Machine (LSSVM), and Extra Tree (ET), is employed. To assess and compare the proposed models, statistical parameters including coefficient of determination (R-2), root mean square error (RMSE), mean absolute error (MAE), and standard deviation (SD) were employed. Accordingly, in the case of depleted fields, the GPR, LSSVM, and ET paradigms show overall R-2, RMSE, and MAE of 0.999999998, 4.75E-06, 0.00021, and 0.00021. For salt domes, the GPR, LSSVM, and ET models indicate overall R-2, RMSE, and MAE of 0.987, 0.0046, and 0.11. Finally, for aquifers, the GPR, LSSVM, and ET algorithms represent overall R-2, RMSE, and MAE of 0.999999997, 7.1094E-06, and 0.0002102. The prediction performance reveals that the GPR model is superior to the LSSVM and ET models. This study found that the proposed intelligent models could be utilized as a template for fast estimating the deliverability of UNGS in depleted fields, aquifers, and salt domes with high accuracy. In the end, the outcomes of this study contribute to a deeper understanding of the critical role of machine learning in resolving the difficulty of forecasting the UNSG on cleaner production and sustainable development strategies. (C) 2022 The Authors. Published by Elsevier Ltd. |
| 学科主题 | Energy & Fuels |
| 语种 | 英语 |
| WOS记录号 | WOS:000836292700002 |
| 出版者 | ELSEVIER |
| 源URL | [http://119.78.100.198/handle/2S6PX9GI/35528]  |
| 专题 | 中科院武汉岩土力学所 |
| 作者单位 | 1.John von Neumann Faculty of Informatics, Obuda University, 1034 Budapest, Hungary 2.School of Earth and Environmental Sciences, Seoul National University, 3.School of Geology, College of Science, University of Tehran, Tehran, Iran 4.State Key Laboratory for Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan 430071, Hubei Province, China 5.Future Technology Research Center, College of Future, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan 6.Institute of Information Engineering, Automation and Mathematics, Slovak University of Technology in Bratislava, Bratislava, Slovakia 7.Gwanak-ro, Gwanak-gu, Seoul, South Korea |
| 推荐引用方式 GB/T 7714 | Hung Vo Thanh,Safaei-Farouji, Majid,Wei, Ning,et al. Knowledge-based rigorous machine learning techniques to predict the deliverability of underground natural gas storage sites for contributing to sustainable development goals[J]. ENERGY REPORTS,2022,8(-):7643. |
| APA | Hung Vo Thanh,Safaei-Farouji, Majid,Wei, Ning,Band, Shahab S.,&Mosavi, Amir.(2022).Knowledge-based rigorous machine learning techniques to predict the deliverability of underground natural gas storage sites for contributing to sustainable development goals.ENERGY REPORTS,8(-),7643. |
| MLA | Hung Vo Thanh,et al."Knowledge-based rigorous machine learning techniques to predict the deliverability of underground natural gas storage sites for contributing to sustainable development goals".ENERGY REPORTS 8.-(2022):7643. |
入库方式： OAI收割
来源：武汉岩土力学研究所
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