Microscopic-scale modeling of CO₂ sequestration and enhanced oil recovery: Methods, mechanisms, and challenges

Abstract

Tackling climate change urgently demands innovative carbon capture, utilization, and storage (CCUS) strategies. CO2-enhanced oil recovery (EOR) serves a dual purpose by enhancing decarbonization and energy security. Optimizing these technologies requires understanding microscopic mechanisms-adsorption, mineralization, wettability alteration, and multiphase transport-that control reservoir-scale behavior. This review synthesizes advances in computational tools-molecular dynamics (MD), density functional theory (DFT), deep learning (DL), and Monte Carlo (MC) methods-used to unravel these complex interactions. MD resolves nanoconfined fluid dynamics and interfacial phenomena under reservoir conditions, while DFT provides quantum-level insights into adsorption energetics and reaction pathways at mineral-fluid interfaces. DL enables rapid property prediction, inverse material design, and surrogate modeling, and MC efficiently predicts thermodynamic equilibria in nanoporous media. Collectively, these tools bridge atomic scale chemistry to pore-scale phenomena, predicting CO2 trapping, mineralization, and hydrocarbon displacement. Integrating these approaches overcomes individual limitations, enabling high-fidelity modeling from atomic bonds to pore-scale transport. Key challenges persist, however: bridging time scales from femtoseconds to field operations; addressing data scarcity in heterogeneous geological systems; and reconciling simulations with macroscopic observations. Emerging solutions involve ML-based interatomic potentials, adaptive sampling, and exascale computing for billion-atom mesoscale models. The future of CO2-EOR and sequestration lies in tightly coupled frameworks integrating molecular simulations, reactive transport models, and real-time field data. Field projects provide essential validation benchmarks, while community databases and federated learning enhance model generalizability. Ultimately, model-guided experimental design, informed by DL-predicted materials and MD-derived fluid behavior, will accelerate robust, scalable, permanent CCUS and EOR strategies.

Document Type: Invited review

Cited as: Yang, Y., Wang, D., He, S., Zhu, C., Lau, D., Zhang, T., Gong, L. Microscopic-scale modeling of CO₂ sequestration and enhanced oil recovery: Methods, mechanisms, and challenges. Computational Energy Science, 2025, 2(1): 10-16. https://doi.org/10.46690/compes.2025.01.03

References

  Allen, M. P., Tildesley, D. J. Computer simulation of liquids. Oxford, UK, Oxford University Press, 1987.

  Andreeva, A., Afanasyev, A. Monte carlo simulation of the CO₂ flooding efficiency at a core scale for different oil compositions. Energies, 2024, 17(10): 2259.

  Burke, K., Wagner, L. O. DFT in a nutshell. International Journal of Quantum Chemistry, 2013, 113(2): 96-101.

  Camacho Vergara, E. L., Kontogeorgis, G. M., Liang, X. Gas adsorption and interfacial tension with classical density functional theory. Industrial & Engineering Chemistry Research, 2019, 58(14): 5650-5664.

  Chen, S., Liu, J., Zhang, Q., et al. A critical review on deployment planning and risk analysis of carbon capture, utilization, and storage (CCUS) toward carbon neutrality. Renewable and Sustainable Energy Reviews, 2022, 167: 112537.

  Du, X., Salasakar, S., Thakur, G. A comprehensive summary of the application of machine learning techniques for CO₂-enhanced oil recovery projects. Machine Learning and Knowledge Extraction, 2024, 6(2): 917-943.

  Diez, J. V. Sampling from molecular unnormalized distributions with deep generative models toward the acceleration of molecular design and conformational sampling with deep learning. Gothenburg, Sweden, Chalmers Tekniska Hogskola, 2024.

  Dehghani, M. R., Ghazi, S. F., Kazemzadeh, Y. Interfacial tension and wettability alteration during hydrogen and carbon dioxide storage in depleted gas reservoirs. Scien tific reports, 2024, 14(1): 11594.

  Frenkel, D., Smit, B. Understanding molecular simulation: From algorithms to applications. Amsterdam, Nether lands, Elsevier, 2023.

  Hill, L. B., Li, X., Wei, N. CO₂-EOR in china: A comparative review. International Journal of Greenhouse Gas Control, 2020, 103: 103173.

  Liu, F., Gao, X., Du, J., et al. Microscopic mechanism of enhancing shale oil recovery through CO₂ flooding insights from molecular dynamics simulations. Journal of Molecular Liquids, 2024, 125593.

  Li, H., Tang, M., Wang, L., et al. Molecular simulation combined with DFT calculation guided heteroatom-doped biochar rational design for highly selective and efficient CO₂ capture. Chemical Engineering Journal, 2024, 481: 148362.

  Lutsko, J. F. Recent developments in classical density functional theory. Advances in chemical physics, 2010, 144(1): 1-92.

  LeCun, Y., Bengio, Y., Hinton, G. Deep learning. Nature, 2015, 521(7553): 436-444.

  Melzer, L. S. Carbon dioxide enhanced oil recovery (CO₂ EOR): Factors involved in adding carbon capture, utilization and storage (CCUS) to enhanced oil recovery. Center for Climate and Energy Solutions, 2012: 1-17.

  Mirzaei-Paiaman, A., Okuno, R. Critical review on wettability, optimal wettability, and artificial wettability alteration in rock-brine-CO₂ systems for geologic carbon sequestration. Gas Science and Engineering, 2024, 132: 205499.

  Marshall, B. D., Cox, K. R., Chapman, W. G. A classical density functional theory study of the neat n-alkane/water interface. The Journal of Physical Chemistry C, 2012, 116(33): 17641-17649.

  Núñez-López, V., Moskal, E. Potential of CO₂-EOR for near-term decarbonization. Frontiers in Climate, 2019, 1.

  Nagao, M., Yao, C., Onishi, T., et al. An efficient deep learning-based workflow for CO₂ plume imaging considering model uncertainties with distributed pressure and temperature measurements. International Journal of Greenhouse Gas Control, 2024, 132: 104066.

  Tapia, J. F. D., Lee, J. Y., Ooi, R. E., et al. A review of optimization and decision-making models for the planning of CO₂ capture, utilization and storage (CCUS) systems. Sustainable Production and Consumption, 2018, 13: 115.

  Zhang, T., Sun, S. An exploratory multi-scale framework to reservoir digital twin. Advances in Geo-Energy Research, 2021, 5(3): 239-251.