A Reliable Correlation To Predict the Change in Minimum Miscibility Pressure When CO2 Is Diluted With Other Gases
Bibliographic record
Abstract
Summary A new genetic algorithm (GA)-based correlation has been developed to estimate the change in MMP when CO2 is diluted with other gases, termed "impure CO2" in the context of this paper. The advantage of this correlation over others is that it can be used for gas mixtures with higher N2 concentrations (tested up to 20 mol%) and with non-CO2 component concentrations up to 78 mol% (e.g., H2S, N2, SOx, O2, and C1−C4) with a higher accuracy. Equally important, it could be a useful screening tool when experimental data are not available and when developing an optimal and economical laboratory program to estimate the MMP. In developing this correlation, the GA software developed in our earlier work (Emera and Sarma 2005a) has been modified to account for various components in the injected-gas stream. The correlation estimates the change in MMP as a function of injected-gas solvency in the oil. The solvency, in turn, is related to critical properties of the injected gas (critical temperature and pressure). In addition, pure CO2/oil MMP is used as an input in this correlation. The correlation has been validated successfully against published experimental data and several correlations in the literature. It yielded a better match with an average error of 4.7% and a standard deviation of 6.3%, followed by the Sebastian et al. (1985) correlation with a 13.1% average error and a 22.0% standard deviation and the Alston et al. (1985) correlation with a 14.1% average error and a 43.2% standard deviation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".