A modified cell-to-cell simulation model to determine the minimum miscibility pressure in tight/shale formations
Bibliographic record
Abstract
A new oil–gas Minimum Miscibility Pressure (MMP) calculation algorithm is developed in this work based on the classic cell-to-cell simulation model. The proposed algorithm couples the effects of capillary pressure and confinement in the original cell-to-cell simulation model to predict the oil–gas MMPs in a confined space. Given that the original cell-to-cell algorithm relies on the volume predictions of the reservoir fluids in each cell, a volume-translated Peng-Robinson Equation of State (PR-EOS) is applied in this work for improved accuracy on volume calculations of the reservoir fluids. The robustness of the proposed algorithm is examined by performing the confined MMP calculations for four oil–gas systems. The tie-line length extrapolation method is used to determine the oil–gas MMP in confined space. The oil recovery factor calculated by the proposed MMP calculation algorithm is then used to validate the results. First, to achieve stable modeling results for all four examples, a total cell number of 500 is determined by examining the variations in the oil recovery as a function of cell number. Then, by calculating the oil recovery factor near the MMP region, it is found that the MMP determined by tie-line length method is slightly lower than the inflection point of the oil recovery curve. Through the case studies, the effects of temperature, pore radius, and injection gas impurity on the confined oil–gas MMP calculations are studied in detail. It is found that the oil–gas MMP is reduced in confined space and the degree of this reduction depends on the pore radius. For all the tested pore radii, the confined MMP first increases and then decreases with an increasing temperature. Furthermore, compared to pure carbon dioxide (CO 2 ) injection, the addition of methane (CH 4 ) in the injection gas increases the oil–gas MMP in confined nanopores. Therefore, it is recommended to control the content of CH 4 in the injection gas in order to achieve a more efficient gas injection design.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".