A Modified Multiple-Mixing-Cell Method with Sub-Cells for MMP Determinations
Bibliographic record
Abstract
Minimum miscible pressure (MMP) is an essential design parameter of gas flooding for enhanced oil recovery (EOR) applications. Researchers have developed a number of methods for MMP computations, including the analytical methods, the slim-tube simulation method, and the multiple-mixing-cell (MMC) method. Among these methods, the MMC method is widely accepted for its simplicity, robustness, and moderate computational cost An important version of the MMC method is the Jaubert et al. method which has a much lower computational cost than the slim-tube simulation method. However, the original Jaubert et al. method suffers several drawbacks. One notable drawback is that it cannot be applied to the scenario where the oil-gas MMP is lower than the saturation pressure of the crude oil. In this work, we present a modified MMC method that is more versatile and robust than the original version. Our method can handle the scenario where the oil-gas MMP is lower than the saturation pressure of the crude oil. Besides, we propose a modified MMC model that can reduce the computational cost of MMP estimations. This modified model, together with a newly proposed pressure search algorithm, increases the MMP estimation accuracy of the modified method. We demonstrate the good performance of the modified MMC method by testing it in multiple case studies. A good agreement is obtained between the MMPs calculated by the modified method and the tie-line-based ones from the literature.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".