Modeling of Multiphase Behavior for Water/n-Alkane Mixtures by Use of the Peng-Robinson EOS
Bibliographic record
Abstract
Abstract Experimental results in the literature show that the water solubility in the oleic (L) phase can be high at reservoir conditions in thermal oil recovery processes; e.g., 24 mol% in the water/n-eicosane binary system at 41 bars and 523 K. It becomes even more significant as the L phase becomes more aromatic, which is the case with heavy oil and bitumen. Efficient and accurate representation of multiphase behavior, which consists of the L, vapor (V), and aqueous (W) phases, is crucial in reliable numerical simulation of steam injection processes. This research presents multiphase behavior predictions for water/n-alkane mixtures by use of the Peng-Robinson equation of state (PR EOS) with the van der Waals mixing rules. Binary interaction parameters (BIPs) are first optimized for water with n-alkanes in terms of three-phase curves including upper critical endpoints (UCEPs), where the V phase and the less dense liquid phase merge in the presence of the denser liquid phase. A new correlation is then developed on the basis of the optimized BIP values. Thermodynamic predictions from the PR EOS with the new BIP correlation are given for various mixtures and compared with experimental data available in the literature. Results show that the PR EOS with the BIP correlation yields reasonable predictions for multiphase behavior of water/n-alkane mixtures. It gives the transition of binary phase behavior between types IIIa and IIIb that is consistent with experimental results. It also can reproduce asymptotic behavior of three-phase curves and the water solubilities in the L phase (xwL) that has been observed as n-alkane becomes heavier. When applied to water-containing reservoir fluids, the PR EOS with the BIP correlation results in a systematic underprediction of xwL. This is expected considering that reservoir oil consists of various types of hydrocarbons and that the affinity towards water is lowest for n-alkanes and highest for aromatics. Accurate xwL predictions can be obtained by systematically reducing the BIP values from the correlation. The correlation developed may serve as the upper limit of BIPs for water with pseudo components that are required for characterizing water-containing reservoir fluids.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".