Evaluating Phase Boundaries and Physical Properties of Solvents/Water/Heavy Oil Systems Under Reservoir Conditions with the Truncated Perturbed-Chain Polar Statistical Associating Fluid Theory (tPC-PSAFT) Equation of State
Bibliographic record
Abstract
Abstract In this work, the truncated perturbed-chain polar statistical associating fluid theory equation of state (tPC-PSAFT EOS) has been integrated and extended to quantify phase behaviour and physical properties of solvents/water/heavy oil systems under reservoir conditions. Experimentally, constant composition expansion (CCE) tests were conducted to measure saturation pressure (Psat), phase volumes, and phase compositions across pressures up to 20 MPa and temperatures reaching 433.2 K for the aforementioned systems with solvents including C3H8, n-C4H10, CO2, and dimethyl ether (DME) in the absence and presence of water. Theoretically, the tPC-PSAFT EOS is integrated with the characterization of heavy oil into aromatic, saturate, and polyaromatic (ASP) fractions to reproduce the experimental measurements. Such an integrated adjustment incorporates the Gross and Vrabec's dipolar term along with binary interaction parameters (BIPs) to enhance the predictive capability of the theoretical model. The addition of a polar term used to better represent the DME's dipole-dipole (DD) interactions, coupled with its modest polarity, significantly enhances the model's accuracy and robustness. The study confirms that water increases the Psat in binary solvent mixtures, yet interestingly, it reduces Psat in more complex ternary/quaternary mixtures. By minimizing deviations between the measured and calculated Psat to calibrate the BIPs, the modified tPC-PSAFT EOS outperforms the original Peng- Robinson (PR) EOS with an overall RMSRE of 2.94% for the former and 25.78% for the latter.
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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 | 0.000 | 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".