Determination of Multiphase Boundaries and Swelling Factors of Solvent(s)–CO<sub>2</sub>–Heavy Oil Systems at High Pressures and Elevated Temperatures
Bibliographic record
Abstract
A generalized methodology has been proposed and successfully applied to determine multiphase boundaries as well as swelling factors of solvent(s)–CO 2 –heavy oil systems at high pressures and elevated temperatures. Experimentally, two- and three-phase boundaries and swelling factors have been respectively measured by conducting PVT tests in the temperature range of 280.45 to 396.15 K. Theoretically, the Peng–Robinson equation of state (PR EOS) combined with the modified alpha function has been applied to describe phase behavior of the solvent(s)–CO 2 –heavy oil systems. More specifically, an exponential distribution function is used to split a heavy oil sample, whereas the logarithm-type lumping method is employed to group single carbon numbers (SCNs) into multiple carbon numbers (MCNs). The exponents associated with two binary interaction parameter (BIP) correlations are respectively tuned for the alkane solvent-pseudocomponent pair and CO 2 -pseudocomponent pair to match the measured saturation pressures. It is found that six pseudocomponents combined with the BIP correlation as a function of critical volume is sufficient to predict saturation pressure with an absolute average relative deviation (AARD) of 5.07%. In addition, the PR EOS model associated with the selected parameters is applied to predict three-phase boundaries for a C 3 H 8 –CO 2 –heavy oil mixture and a n -C 4 H 10 –CO 2 –heavy oil mixture yielding an overall AARD of 4.58%. As for swelling factors, the Peneloux et al. method provides the minimum AARD of 2.09% in comparison with 4.00% from the Jhaveri et al. method and 2.41% from the Twu et al. method, respectively.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".