CO<sub>2</sub>‐oil saturation pressure and onset asphaltene precipitation
Bibliographic record
Abstract
Partial miscible flooding using CO2 injection has been shown to be a promising method for enhanced oil recovery, but this injection could result in asphaltene precipitation, which clogs the reservoir and production equipment. Therefore, an important parameter to be monitored is the onset of asphaltene precipitation. The efficient displacement of oil by CO2 depends on a variety of factors, including phase behaviour of CO2/crude‐oil mixtures that requires an accurate description of the CO2‐oil mixture bubble pressure. Observing bubble pressure behaviour, it is possible to exactly determine the CO2 molar fraction for onset asphaltene precipitation, which is expected to occur when the selected injection pressure is equal to the bubble pressure at the same CO2 molar fraction. However, assessment of oil characterization relating CO2‐oil mixture bubble pressure and onset asphaltene precipitation has not been found in the literature and should be properly addressed. In this work, we employed the Soave‐Redlich‐Kwong equation of state and used CO2‐oil mixture bubble pressure experimental data from literature to show that the accuracy of onset asphaltene precipitation is quite affected by the binary interaction parameter between methane and C7+ fractions, and the density of this heavy fraction.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".