Experimental and Theoretical Determination of Diffusion Coefficients of CO2-Heavy Oil Systems by Coupling Heat and Mass Transfer
Bibliographic record
Abstract
By treating heavy oil as multiple pseudocomponents, techniques have been developed to experimentally and theoretically determine diffusion coefficients of CO2-heavy oil systems by coupling heat and mass transfer together with consideration of swelling effect. Experimentally, diffusion tests have been conducted for hot CO2-heavy oil systems with three different temperatures under a constant pressure by using a visualized pressure-volume-temperature (PVT) setup. The swelling of liquid phase in the PVT cell is continuously monitored and recorded during the measurements. Theoretically, a two-dimensional (2D) mathematical model incorporating the volume-translated Peng–Robinson equation of state (PR EOS) with a modified alpha function has been developed to describe heat and mass transfer for hot CO2-heavy oil systems. Heavy oil sample has been characterized as three pseudocomponents for accurately quantifying phase behavior of the CO2-heavy oil systems, while the binary interaction parameters (BIPs) are tuned with the experimentally measured saturation pressures. The diffusion coefficient of hot CO2 in heavy oil is then determined once the discrepancy between the experimentally measured dynamic swelling factors and theoretically calculated ones has been minimized. During the diffusion experiments, heat transfer is found to be dominant over mass transfer at the beginning and reach its equilibrium in a shorter time; subsequently, mass transfer shows its dominant effect. The enhanced oil swelling mainly occurs during the coupled heat and mass transfer stage. CO2 diffusion coefficient in heavy oil is found to increase with temperature at a given pressure, while it can be explicitly correlated as a function of temperature.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".