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Review of deep learning algorithms in molecular simulations and perspective applications on petroleum engineering

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In the last few decades,deep learning(DL)has afforded solutions to macroscopic problems in petroleum engineering,but mechanistic problems at the microscale have not benefited from it.Mechanism studies have been the strong demands for the emerging projects,such as the gas storage and hydrate production,and for some problems encountered in the storage process,which are common found as the chemical interaction between injected gas and mineral,and the formation of hydrate.Emerging advances in DL technology enable solving molecular dynamics(MD)with quantum accuracy.The conventional quantum chemical method is computational expensive,whereas the classical MD method cannot guarantee high accuracy because of its empirical force field parameters.With the help of the DL force field,precision at the quantum chemistry level can be achieved in MD.Moreover,the DL force field promotes the com-putational speed compared with first-principles calculations.In this review,the basic knowledge of the molecular force field and deep neural network(DNN)is first introduced.Then,three representative open-source packages relevant to the DL force field are introduced.As the most common components in the development of oil and gas reservoirs,water and methane are studied from the aspects of computational efficiency and chemical reaction respectively,providing the foundation of oil and gas researches.However,in the oil and gas problems,the complex molecular topo structures and various element types set a high challenge for the DL techniques in MD.Regarding the computational efficiency,it needs improvement via GPU and parallel accelerations to compete with classical MD.Even with such difficul-ties,the booming of this technique in the area of petroleum engineering can be predictable.

Deep neural networkMolecular dynamicsForce fieldPetroleum engineering

Jie Liu、Tao Zhang、Shuyu Sun

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Physical Science and Engineering Division(PSE),Computational Transport Phenomena Laboratory,King Abdullah University of Science and Technology(KAUST),Thuwal 23955-6900,Saudi Arabia

国家自然科学基金King Abdullah University of Science and Technology(KAUST)King Abdullah University of Science and Technology(KAUST)King Abdullah University of Science and Technology(KAUST)resources of the Supercomputing Laboratory at King Abdullah University of Science and Technology(KAUST)in Thuwal,Saudi Arabi

51936001BAS/1/1351-01URF/1/4074-01URF/1/3769-0

2024

10.1016/j.gsf.2023.101735

地学前缘(英文版)
中国地质大学（北京） 北京大学

地学前缘(英文版)

CSTPCD
影响因子：0.576
ISSN：1674-9871
年,卷(期)：2024.15(2)
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