Advances in multiscale atomistic modelling for enhanced oil and gas recovery and CO₂ sequestration

Abstract

A thorough understanding of gas-fluid interactions, competitive adsorption mechanisms, transport behaviour, and reactive transformations is fundamental for improving enhanced oil and gas production and ensuring permanent CO₂ storage. The combined application of classical and ab initio molecular dynamics, grand-canonical Monte Carlo, and emerging machine learning approaches enables a comprehensive elucidation these mechanisms across multiple spatial and temporal scales. This view integrates our recent multiscale modelling efforts with state-of-the-art computational techniques to chart an agenda for predictive subsurface workflows.

Document Type: Perspective

Cited as: Shi, J., Zhang, T. Advances in multiscale atomistic modelling for enhanced oil and gas recovery and CO₂ sequestration. Computational Energy Science, 2025, 2(1): 1-3. https://doi.org/10.46690/compes.2025.01.01

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