An improved correlation to determine minimum miscibility pressure of CO2–oil system
Bibliographic record
Abstract
An accurate and reliable estimation of minimum miscibility pressure (MMP) of CO2–oil system is a critical task for the design and implementation of CO2 miscible displacement process. In this study, an improved CO2–oil MMP correlation was developed to predict the MMP values for both pure and impure CO2 injection cases based on ten influential factors, i.e. reservoir temperature (TR), molecular weight of C7+ oil components (MWC7+), mole fraction of volatile oil components (xvol), mole fraction of C2C4 oil components (xC2-C4), mole fraction of C5C6 oil components (xC5-C6), and the gas stream mole fractions of CO2 (yCO2), H2S (yH2S), C1 (yC1), hydrocarbons (yHC) and N2 (yN2). The accuracy of the improved correlation was evaluated against experimental data reported in literature concurrently with those estimated by several renowned correlations. It was found that the improved correlation provided higher prediction accuracy and consistency with literature experimental data than other literature correlations. In addition, the predictive capability of the improved correlation was further validated by predicting an experimentally measured CO2–oil MMP data, and it showed an accurate result with the absolute deviation of 4.15%. Besides, the differential analysis of the improved correlation was analyzed to estimate the impact of parameters uncertainty in the original MMP data on the calculated results. Also, sensitivity analysis was performed to analyze the influence of each parameter on MMP qualitatively and quantitatively. The results revealed that the increase of xC2-C4, xC5-C6 and yH2S lead to the decrease of MMP, while the increase of TR, MWC7+, xvol, yCO2, yC1, yHC and yN2 tend to increase the MMP. Overall, the relevance of each parameter with MMP follows the order of TR > xC5-C6 > MWC7+ > xvol > yH2S > yHC > yCO2 > yC1 > yN2 > xC2-C4.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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".