Robust Three-Phase Vapor–Liquid–Asphaltene Equilibrium Calculation Algorithm for Isothermal CO<sub>2</sub> Flooding Applications
Bibliographic record
Abstract
CO 2 flooding is an effective enhanced oil recovery process for light oil reservoirs. Asphaltenes can easily precipitate during CO 2 flooding, leading to the appearance of three-phase vapor–liquid–asphaltene (VLS) equilibria. A prerequisite for accurately simulating the CO 2 flooding process is developing a robust three-phase VLS equilibrium calculation algorithm. In this study, we develop a robust and efficient three-phase VLS equilibrium calculation algorithm with the use of asphaltene precipitation model proposed by Nghiem et al. [ Efficient Modelling of Asphaltene Precipitation, SPE, 1993 ]. To develop this algorithm, a two-phase flash calculation algorithm is first developed to split the mixture into an asphaltene phase and a nonasphaltene phase. Moreover, two different three-phase VLS flash calculation algorithms are developed and incorporated into our three-phase equilibrium calculation algorithm. New initialization approaches for both stability test and flash calculations are proposed. The performance of this three-phase VLS equilibrium calculation algorithm is tested by generating pressure–composition ( P – X ) diagrams for several reservoir fluids mixed with pure or impure CO 2 .
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".