Effects of Four Important Factors on the Measured Minimum Miscibility Pressure and First-Contact Miscibility Pressure
Bibliographic record
Abstract
The vanishing interfacial tension (VIT) technique is applied to determine the minimum miscibility pressures (MMPs) and first-contact miscibility pressures ( P max ) of five light crude oil–CO 2 systems from the measured equilibrium interfacial tensions (IFTs) at different equilibrium pressures. The equilibrium IFTs are measured under various experimental conditions by applying the axisymmetric drop shape analysis technique for the pendant drop case. It is found that in each IFT test, the measured equilibrium IFT is reduced almost linearly with the equilibrium pressure in two pressure ranges. The MMP of each light crude oil–CO 2 system is thus determined from the measured equilibrium IFTs in range I by applying the VIT technique. The first-contact miscibility pressure ( P max ) of each light crude oil–CO 2 system is extrapolated from the measured equilibrium IFTs in range II. Moreover, the test temperature, crude oil composition (dead/live oil), gas composition (pure/impure CO 2 ), and initial gas–oil ratio (GOR) are studied to examine their specific effects. The experimental data show that the measured MMPs and P max values of five light crude oil–CO 2 systems increase linearly with the temperature. The presence of CH 4 in the CO 2 phase results in substantially higher MMP and P max . An increased initial GOR leads to marginally higher MMP and P max .
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".