Mechanism Insights into the Miscibility Behavior of CO <sub>2</sub> /N <sub>2</sub> -Multicomponent Hydrocarbon–Surfactant Systems under Reservoir Conditions
Bibliographic record
Abstract
The high miscibility of oil with CO 2 significantly enhances its mobility and thus improves oil recovery efficiency during CO 2 flooding. Many factors have strong influence on miscibility, including the chain length of oil, the component of injected gas, reservoir conditions, etc. Despite their critical role, the mechanisms underlying these influences remain poorly understood due to challenges in analyzing pore-scale mixing dynamics. Therefore, the molecular dynamics method was employed, and mixed gas–surfactant–mixed oil models were established to investigate the influence of different factors on the miscibility behavior during the multicomponent gas flooding process. Nonionic CO 2 friendly polyoxypropylene alkyl ether (POP) was chosen as a surfactant, and its influence on interfacial tension was also explored. The research results indicate a strong hindering effect of long-chain alkanes toward CO 2 –oil miscibility. When its content is reduced to less than 30%, the hindering effect started to diminish. As for the gas component, less than 15% N 2 is beneficial for the movement of CO 2 and accelerates the miscibility process. In addition, as the chain length of POP increases, its ability to improve the miscibility of CO 2 and oil first increases and then decreases. The results also indicate that an optimized structure of POP with 8 PO groups has the highest ability to assist the CO 2 –oil mixing process. Compared to the system without POP, the IFT can be reduced by approximately 46.78%. The simulation results can provide useful guidance for enhancing oil recovery in deep, low-permeability, and tight reservoirs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".