Investigation of the minimum miscibility pressure for injection of two different gases into two Iranian oil reservoirs: Experimental and theory
Bibliographic record
Abstract
Abstract The results of the minimum miscibility pressure (MMP) determination for miscible injection of two gases (CO2 and an associated gas of one of Iranian gas reservoirs) into two different oil samples from two Iranian oil reservoirs using a slim tube apparatus are presented in this work. For efficient determination of MMP, prior to slim tube experimentation, cell‐to‐cell simulation of the slim tube experiment was performed using a tuned Peng Robinson equation of state as a pre‐experiment and the results were used as initial estimates of MMP to select the pressure steps for the slim tube experiment. Finally, a comparison between the measured MMP values obtained by the slim tube experiments and those calculated by cell‐to‐cell simulation was made. It was shown that the cell‐to‐cell slim tube simulation predicts the results of slim tube experiments with a relative error of less than 6 %. This low error value shows that cell‐to‐cell simulation can replace the slim tube test in the cases where time is a major concern. Moreover, since the slim tube is an expensive and time‐consuming experiment and selecting the pressures to run the test is very important, cell‐to‐cell simulation can help us select the pressures for performing a slim tube experiment.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".