Determination of CO<sub>2</sub> Minimum Miscibility Pressure from Measured and Predicted Equilibrium Interfacial Tensions
Bibliographic record
Abstract
Accurate determination of the minimum miscibility pressure (MMP) of a crude oil−CO 2 system at the actual reservoir temperature is required in order to determine whether CO 2 flooding is immiscible or miscible under the actual reservoir pressure. The objective of this study is to determine the MMPs of a crude oil−CO 2 system from its measured and predicted equilibrium interfacial tension (IFT) versus equilibrium pressure data at a constant temperature. In the experiment, first, the CO 2 solubilities in the crude oil are measured under four different equilibrium pressures. Second, the equilibrium IFTs of the crude oil−CO 2 system are measured at 12 different equilibrium pressures and a constant temperature of T = 27 °C by applying the axisymmetric drop shape analysis (ADSA) technique for the pendant drop case. The detailed experimental results show that the CO 2 solubility in the crude oil is increased almost linearly with the equilibrium pressure. It is also found that the measured crude oil−CO 2 equilibrium IFT is reduced almost linearly with the equilibrium pressure as long as it is lower than a threshold pressure. The measured equilibrium IFT versus equilibrium pressure data are used to determine the MMP of the crude oil−CO 2 system by applying the so-called vanishing interfacial tension (VIT) technique. In addition, the equilibrium IFT versus equilibrium pressure data of the crude oil−CO 2 system are predicted by using the parachor model and linear gradient theory (LGT) model, respectively. The predicted equilibrium IFT data from each model are also used to determine the MMP of the same crude oil−CO 2 system. Comparison of the MMPs determined from the two equilibrium IFT prediction models and that determined from the measured equilibrium IFTs shows that the LGT model is suitable for determining the MMP of the crude oil−CO 2 system.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".