Improved minimum miscibility pressure correlation for CO<sub>2</sub> flooding using various oil components and their effects
Bibliographic record
Abstract
Carbon dioxide (CO2) flooding is an effective method of enhanced oil recovery (EOR) that has become one of the most important EOR processes. One of the key factors in the design of a CO2 injection project is the minimum miscibility pressure (MMP), whereas local sweeping efficiency during gas injection is dependent on the MMP. There are many empirical correlation analyses for the MMP calculation. However, these analyses focus on the molecular weight of the C5+ or C7+ fraction, and do not emphasize the effects of various components on MMP. Our study aims to develop an improved CO2–oil MMP correlation analysis that includes parameters such as reservoir temperature and various oil mole fractions. Here, correlation analysis was performed to define the influence of various components on the MMP using various data from 45 oilfields which have experimental CO2–oil MMP and oil compositions readily available. Thirty of the data sets were used to develop an improved correlation, and the other 15 data sets were used to verify the correlation. It was found that the mole fraction of C3 and C6 were the main factors that affected MMP. There was a good quadratic polynomial relationship between the mole fraction of C3 and MMP, and the relationship also existed between the mole fraction of C6 and MMP. The results do not include the molecular weight of the C5+ or C7+ fraction like other common correlations. Nine popular correlations were then used to also predict the MMP, and the comparison showed that the improved CO2–oil MMP correlation defined here was a better estimate. The correlation was then used in Dongshisi and Fuyu oilfields to assess EOR potential, the results also indicated that MMP increased over the course of the CO2 flooding process. This increase shows that it would be more difficult to achieve a mixed phase between crude oil and CO2, therefore the oil recovery would be difficult to further improve towards the end of injection.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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".