A Robust Four-Phase Equilibrium Calculation Algorithm for Hydrocarbon-Water Mixtures at Pressure and Enthalpy Specifications
Bibliographic record
Abstract
Abstract A robust multiphase equilibrium calculation algorithm with pressure and enthalpy (PH) specifications, i.e., an isenthalpic algorithm, plays an important role in the compositional simulations of steam-based enhanced oil recovery (EOR) applications (such as steam and solvent co-injection process for heavy oil recovery). Up to now, there are few works documented in the literature focusing on four-phase isenthalpic algorithms. In this paper, we propose a four-phase isenthalpic algorithm with a nested approach. It contains an inner loop and an outer loop. In the inner loop, a well-designed isobaric/isothermal (PT) multiphase (up to four phases) equilibrium algorithm is employed to solve the phase fractions and compositions, while the Brent's method (1971) is applied in the outer loop to update the temperature by satisfying the energy conservation equation. We test the performance of the proposed algorithm using four case studies under different pressure-enthalpy conditions. Calculation results demonstrate that the proposed PH algorithm is always able to converge to the correct phase equilibria with only tens of PT algorithm calls.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".