Improving predictions of shale wettability using advanced machine learning techniques and nature-inspired methods: Implications for carbon capture utilization and storage.

Autor: Zhang H; College of Safety Science and Engineering, Liaoning Technical University, Huludao 125105, China; Key Laboratory of Mine Thermodynamic Disasters and Control of Ministry of Education, Huludao 125105, China. Electronic address: zhanghemeng@lntu.edu.cn., Thanh HV; Laboratory for Computational Mechanics, Institute for Computational Science and Artificial Intelligence, Van Lang University, Ho Chi Minh City, Viet Nam; Faculty of Mechanical-Electrical and Computer Engineering, School of Technology, Van Lang University, Ho Chi Minh City, Viet Nam. Electronic address: hung.vothanh@vlu.edu.vn., Rahimi M; Department of Biosystems Engineering, Ferdowsi University of Mashhad, Mashhad, Iran., Al-Mudhafar WJ; Basrah Oil Company, Iraq., Tangparitkul S; Department of Mining and Petroleum Engineering, Chiang Mai University, Thailand., Zhang T; State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University, Chendu, China., Dai Z; School of Environmental and Municipal Engineering, Qingdao University of Technology, Qingdao 266520, China; College of Construction Engineering, Jilin University, Changchun, China., Ashraf U; Institute for Ecological Research and Pollution Control of Plateau Lakes, School of Ecology and Environmental Science, Yunnan University, Kunming 650504, China.
Jazyk: angličtina
Zdroj: The Science of the total environment [Sci Total Environ] 2023 Jun 15; Vol. 877, pp. 162944. Date of Electronic Publication: 2023 Mar 20.
DOI: 10.1016/j.scitotenv.2023.162944
Abstrakt: The utilization of carbon capture utilization and storage (CCUS) in unconventional formations is a promising way for improving hydrocarbon production and combating climate change. Shale wettability plays a crucial factor for successful CCUS projects. In this study, multiple machine learning (ML) techniques, including multilayer perceptron (MLP) and radial basis function neural networks (RBFNN), were used to evaluate shale wettability based on five key features, including formation pressure, temperature, salinity, total organic carbon (TOC), and theta zero. The data were collected from 229 datasets of contact angle in three states of shale/oil/brine, shale/CO 2 /brine, and shale/CH 4 /brine systems. Five algorithms were used to tune MLP, while three optimization algorithms were used to optimize the RBFNN computing framework. The results indicate that the RBFNN-MVO model achieved the best predictive accuracy, with a root mean square error (RMSE) value of 0.113 and an R 2 of 0.999993. The sensitivity analysis showed that theta zero, TOC, pressure, temperature, and salinity were the most sensitive features. This research demonstrates the effectiveness of RBFNN-MVO model in evaluating shale wettability for CCUS initiatives and cleaner production.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2023 Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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