A Modified Multiple-Mixing-Cell Algorithm for Minimum Miscibility Pressure Prediction with the Consideration of the Asphaltene-Precipitation Effect
Bibliographic record
Abstract
Minimum miscibility pressure (MMP) is one of the most important design parameters in CO 2 flooding. Recent experimental studies show that asphaltene precipitation in light oils increases the MMP between injection gas and crude oil. To model the asphaltene-precipitation effect on MMP calculations, we develop a three-phase multiple-mixing-cell (MMC) algorithm by considering three-phase vapor–liquid-asphaltene (VLS) equilibria. This algorithm is developed by modifying the MMC method proposed by Ahmadi and Johns ( Soc. Pet. Eng. J. 2011, 16, 4, 733). The three-phase VLS equilibrium calculation algorithm developed by Li and Li ( Ind. Eng. Chem. Res. 2019, in press) is integrated into the two-phase MMC algorithm. Several example calculations are carried out to demonstrate the performance of our algorithm. The MMPs predicted by our algorithm and those predicted by the two-phase MMC algorithm are both compared with the MMPs measured by experiments. Comparison results show that MMPs predicted by our algorithm have a slightly better agreement with the experimental results than those predicted by the two-phase MMC algorithm.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".