The Effect of Small Molecule Gas-Soluble Polymers on Minimum Miscibility Pressure During CO2 Injection
Bibliographic record
Abstract
Abstract The minimum miscibility pressure is the main technical challenges during CO2 flooding. Therefore, the purpose of this work is to explore the effect of small molecule CO2-soluble polymers including PDMS, PFA, P-1-D, and PVEE on minimum miscibility pressure during gas flooding. The dilute concentrations of polymers (1000 to 50000 ppm) were considered to dissolve in CO2 at 60 and 80 °C. According to the cloud point pressure results, the small molecule CO2-soluble polymers dissolved in carbon dioxide at pressures consistent with gas-based EOR methods (less than 2500 psi) at both 60 and 80 °C. Also, (CO2/polymers)-oil interfacial tensions were decreased extremely via the dissolution of dilute concentrations of polymers in CO2. Moreover, the small molecule CO2-soluble polymers decreased significantly the MMPs by 19.4% for CO2/PFA, 17% for CO2/PDMs, 13.6% for CO2/PVEE, and 10% for CO2/P-1-D scenarios in comparison with pure carbon dioxide injection. Moreover, the 26.5% additional oil was recovered during miscible CO2/PDMS injection in comparison with miscible pure CO2 scenario. Therefore, the small molecule CO2-soluble polymers containing functional groups with oxygen can be one of the best candidates for miscible CO2 displacement in the field-scale.
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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 | 0.001 |
| 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".