A New Diminishing Interface Method for Determining the Minimum Miscibility Pressures of Light Oil–CO<sub>2</sub> Systems in Bulk Phase and Nanopores
Bibliographic record
Abstract
In this paper, a new interfacial thickness-based method, namely, the diminishing interface method (DIM), is developed to determine the minimum miscibility pressures (MMPs) of light oil–CO 2 systems in bulk phase and nanopores. First, a Peng–Robinson equation of state (PR-EOS) is modified to calculate the vapor–liquid equilibrium in nanopores by considering the effects of capillary pressure and shifts of critical temperature and pressure. Second, the parachor model is coupled with the modified PR-EOS to predict the interfacial tensions (IFTs) in bulk phase and nanopores. Third, a formula of the interfacial thickness between two mutually soluble phases is derived, based on which the novel DIM is developed by considering two-way mass transfer across the interface. The MMP is determined by extrapolating the derivative of the interfacial thickness with respect to the pressure (∂δ/∂ P ) T to zero. It is found that the modified PR-EOS coupled with the parachor model is accurate for predicting the phase behavior and IFTs in bulk phase and nanopores. More specifically, in nanopores, the lighter components prefer to be in vapor phase by increasing the temperature or decreasing the pressure and the IFTs are decreased with the pore radius, especially at low pressures. The determined MMPs of 12.4, 15.0, and 22.1 MPa from the DIM agree well with the laboratory measured results for the three Pembina light oil–CO 2 systems in bulk phase at T res = 53.0 °C. Moreover, the MMPs of the Pembina and Bakken live oil–pure CO 2 systems in the nanopores of 100, 20, 4 nm are determined from the DIM, which tend to be decreased at a smaller pore level. Physically, the interface between the light oil and CO 2 diminishes and the two-phase compositional change reaches its maximum at the determined MMP from the DIM.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".