Factors Governing the Enhancement of Hydrocarbon Recovery via H<sub>2</sub>S and/or CO<sub>2</sub> Injection: Insights from a Molecular Dynamics Study in Dry Nanopores
Bibliographic record
Abstract
Abstract Although enhanced oil recovery (EOR) is often achieved by CO2 injection, the use of acid gases has also been attempted, for example, in oil fields in west Canada. To design EOR technologies effectively, it would be beneficial to quantify the molecular mechanisms responsible for enhanced recovery under various conditions. We report here the molecular dynamics simulation results that probe the potential of recovering n-butane confined in silica, muscovite, and magnesium oxide nanopores, all proxies for subsurface materials. The three model solid substrates allow us to identify different molecular mechanisms that control confined fluid behavior and to identify the conditions at which different acid gas formulations are promising. The acid gases considered are CO2, H2S, and their mixtures. For comparison, in some cases, we consider the presence of inert gases such as N2. In all cases, the nanopores are dry. The recovery is quantified in terms of the amount of n-butane displaced from the pore surface as a function of the amount of gases present in the pores. The results show that the gas performance depends on the chemistry of the confining substrate. Whereas CO2 is more effective at displacing n-butane from the protonated silica pore surface, H2S is more effective in muscovite, and both gases show similar performance in MgO. Analysis of the interaction energies between the confined fluid molecules and the surface demonstrates that the performance depends on the gas interaction with the surface, which suggests experimental approaches that could be used to formulate the gas mixtures for EOR applications. The structure of the gas films in contact with the solid substrates is also quantified as well as the self-diffusion coefficient of the fluid species in confinement. The results could contribute to designing strategies for achieving both improved hydrocarbon production and acid gas sequestration.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".