A New Look at the Minimum Miscibility Pressure (MMP) Determination from Slimtube Measurements
Bibliographic record
Abstract
Abstract Slimtube measurement is one of the standard experimental techniques used for determining the minimum miscibility pressure (MMP) of an oil and injection gas system prior to the initiation of an enhanced oil recovery (EOR) project. It is preferred because it involves actual fluid displacement in a porous medium. However, the specific criterion for determining the cut-off point during the measurement is not uniquely agreed upon in the literature. Different criteria have been proposed by researchers and this has been one of the setbacks of using Slimtube measurements. The most commonly used criterion is the 1.2 PV criterion, which uses the recovery after injecting 1.2 pore volumes of the displacing gas as the cut-off. However, experimental observations show that even at supercritical condition, the volume of a gas is a strong function of the experimental pressure. Therefore, there is a need to develop an alternative means of determining the MMP that is not subject to particular pore volumes injected during Slimtube measurements. This work presents different means of determining the MMP, based entirely on recovery and the particular displacement phenomenon. In this approach, two new parameters are defined - the instantaneous recovery rate (IRR) and the oil recovery rate (ORR). The maximum values for these parameters for each experiment are used as the cut-off value. This new criteria was used in analyzing nine experimental data using oil from the Permian Basin. The results were compared with MMP prediction based on maximum recovery from each of the runs and the results were found to be in agreement. These new criteria will provide consistent cut-off point for experimental runs because Slimtube measurements take a long time to complete. The new procedure ensures that adequate data have been gathered during each experimental run, sufficient for a consistent experimental analysis.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".