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

Abstract

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 computational 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 difficulties, the booming of this technique in the area of petroleum engineering can be predictable.

Keywords

Deep neural network / Molecular dynamics / Force field / Petroleum engineering

CRediT authorship contribution statement

Lingyu Zhang: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Resources, Visualization, Writing – original draft, Writing – review & editing. Sampriti Basak: Methodology, Visualization, Writing – original draft, Writing – review & editing. David Zakharov: Data curation, Formal analysis, Methodology, Writing – original draft, Writing – review & editing. Kristoffer Szilas: Conceptualization, Data curation, Project administration, Resources, Supervision, Writing – review & editing.

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.

Acknowledgements

We thank Ole Christiansen, former CEO for NunaMinerals A/S, for providing us with access to the Storø drill cores. Additionally, we appreciate Torsten Vennemann at the University of Lausanne for providing access to the laser fluorination facility. This study forms part of the PhD-thesis of Lingyu Zhang. The authors acknowledge funding from VILLUM FONDEN Grant VKR18978 to Kristoffer Szilas, and China Scholarship Council (No. 202006170037) to Lingyu Zhang. We thank Vincent van Hinsberg for insightful discussions, and Tod Waight for help with EPMA analyses at the University of Copenhagen. We thank two anonymous reviewers for their constructive comments that greatly helped to improve the manuscript, and Associate Editor Vinod Samuel for professional editorial handling.

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