Advances in multiscale atomistic modelling for enhanced oil and gas recovery and CO₂ sequestration
Bibliographic record
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 References Cygan, R. T., Liang, J., Kalinichev, A. G. Molecular models of hydroxide, oxyhydroxide, and clay phases and the development of a general force field. The Journal of Physical Chemistry B, 2004, 108(4): 1255-1266. Fentaw, J. W., Emadi, H., Hussain, A., et al. Geochemistry in geological CO2 sequestration: A comprehensive review. Energies, 2024, 17(19): 5000. Gbadamosi, A. O., Junin, R., Manan, M. A., et al. An overview of chemical enhanced oil recovery: Recent advances and prospects. International Nano Letters, 2019, 9: 171-202. Gong, L., Shi, J., Ding, B., et al. Molecular insight on competitive adsorption and diffusion characteristics of shale gas in water-bearing channels. Fuel, 2020, 278: 118406. Grimme, S. Semiempirical GGA-type density functional constructed with a long-range dispersion correction. Journal of computational chemistry, 2006, 27(15): 1787-1799. Kühne, T. D., Iannuzzi, M., Del Ben, M., et al. CP2K: An electronic structure and molecular dynamics software package-Quickstep: Efficient and accurate electronic structure calculations. The Journal of Chemical Physics, 2020, 152(19): 194103. Martin, M. G., Siepmann, J. I. Transferable potentials for phase equilibria. 1. United-atom description of n-alkanes. The Journal of Physical Chemistry B, 1998, 102(14): 2569-2577. Perdew, J. P., Burke, K., Ernzerhof, M. Generalized gradient approximation made simple. Physical review letters, 1996, 77(18): 3865-3868. Shi, J., Gong, L., Sun, S., et al. Competitive adsorption phenomenon in shale gas displacement processes. Rsc Advances, 2019, 9(44): 25326-25335. Shi, J., Zhang, T., Xie, X., et al. Characterizing competitive ad sorption and diffusion of methane and carbon dioxide in kerogen type-III slit model. Computational Geosciences, 2024, 28(5): 955-965. Shi, J., Zhang, T., Sun, S., et al. Ab initio insights into the CO2 adsorption mechanisms in hydrated silica nanopores. Chemical Engineering Science, 2025, 313: 121741. Sun, H. Compass: An ab initio force-field optimized for condensed-phase applications overview with details on alkane and benzene compounds. The Journal of Physical Chemistry B, 1998, 102(38): 7338-7364. VandeVondele, J., Hutter, J. Gaussian basis sets for accurate calculations on molecular systems in gas and condensed phases. The Journal of chemical physics, 2007, 127(11): 114105. Zhang, T., Zhang, Y., Katterbauer, K., et al. Deep learning assisted phase equilibrium analysis for producing natural hydrogen. International Journal of Hydrogen Energy, 2024, 50: 473-486.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".