Determination of Diffusion Coefficient for Alkane Solvent–CO<sub>2</sub> Mixtures in Heavy Oil with Consideration of Swelling Effect
Bibliographic record
Abstract
A generalized methodology has been developed and successfully applied to determine diffusion coefficient of alkane solvent–CO 2 –heavy oil systems with consideration of swelling effect. Theoretically, a one-dimensional and one-way mass transfer model incorporating the volume translated Peng–Robinson equation of state (PR EOS) has been developed to describe the mass transfer from alkane solvent–CO 2 mixture to heavy oil, which accounts for the oil swelling effect resulted from gas dissolution. The heavy oil sample has been characterized as three pseudocomponents, while the binary interaction parameter (BIP) correlations are tuned with the experimentally measured saturation pressures. Both apparent diffusion coefficients for gas mixtures and individual diffusion coefficient of each component of a mixture are determined once the discrepancy between the measured and calculated dynamic swelling factors of heavy oil has been minimized. The volume translated PR EOS with the three characterized pseudocomponents and the tuned BIP correlations is able to accurately predict the phase behavior of alkane solvent–CO 2 –heavy oil systems. Compared to the apparent diffusion coefficient, better agreements between the measured and calculated dynamic swelling factors have been obtained by use of the individual diffusion coefficients. Addition of C 3 H 8 into CO 2 stream is found to not only diffuse faster into heavy oil than CO 2 but also contribute to a larger degree of oil swelling, leading to a faster and enhanced swelling effect of C 3 H 8 –CO 2 –heavy oil system in comparison with the CO 2 –heavy oil system.
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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 | 0.000 | 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 | 0.001 |
| 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".