Effects of temperature and flue gas composition on miscibility behavior between flue gas and gas condensate
Bibliographic record
Abstract
• Consider unique conditions associated with thermal miscible flooding in this study. • Examine effects of temperature and flue gas composition on miscibility behavior. • MMP of an injection gas first increases and then decreases with temperature. • CO 2 exhibits the lowest peak MMP and N 2 exhibits the highest peak MMP. • Inclusion of N 2 increases peak MMP and dimensionless fraction of vaporizing drive. Thermal miscible flooding has shown promise in enhancing light oil recovery from gas condensate reservoirs. In this process, the generated flue gases could become miscible with gas condensate under elevated reservoir temperatures. To study the miscibility behavior between flue gas and gas condensate, we develop a thermodynamic model based on the Peng-Robinson equation of state (PR EOS) coupled with Peneloux volume translation, for a benchmark gas condensate sample. The pseudocomponents’ properties are tuned to match the constant composition expansion (CCE) and the constant volume depletion (CVD) test data. The tuned model can well reproduce measured data (e.g., relative volume and liquid dropout) in these tests. We then apply it to calculate the phase envelopes of N 2 -gas condensate, CO 2 -gas condensate, and CO 2 N 2 -gas condensate mixtures, revealing the shifts in original phase envelopes. Next, to investigate the influences of temperature and flue gas composition on miscibility behavior, we utilize the analytical tie line method and the cell-to-cell method to calculate the MMPs and drive types of different flue gas-gas condensate mixtures. It is found that the MMP of a given injection gas first increases with temperature, reaches a peak, and then decreases. The dimensionless fraction of vaporizing mechanism of a given injection gas decreases with temperature. The highest MMPs of CO 2 and N 2 are 506 bara at 350 °C and 620 bara at 250 °C, respectively. If the flue gas contains more N 2 , the peak MMP and the dimensionless fraction of vaporizing mechanism increase, but the temperature corresponding to the peak MMP is reduced.
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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) | 0.001 | 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".