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.

Publikováno v: The Science of the total environment [Sci Total Environ] 2023 Jun 15; Vol. 877, pp. 162944. Date of Electronic Publication: 2023 Mar 20.