Quantification of Mutual Mass Transfer Coupled with Heat Transfer for CO2/C3H8-Heavy Oil Systems Under Reservoir Conditions
Bibliographic record
Abstract
Summary Experimental and theoretical methods have been developed to quantitatively assess the heat and bidirectional mass transfer between a hot solvent (i.e., CO2 and C3H8) and heavy oil. Experimentally, we performed diffusion experiments for hot solvent-heavy oil systems at elevated pressures and temperatures using a pressure/volume/temperature (PVT) setup. The dynamic volume change of the oleic phase has been continuously measured and recorded throughout each experiment. At the conclusion of each test, gas chromatography (GC) analysis has been conducted on the collected gas samples. Theoretically, a diffusion model has been integrated with heat transfer to determine the mutual diffusivities for the hot CO2/C3H8-heavy oil systems by incorporating the volume-translated (VT) Peng-Robinson equation of state (PR EOS) together with the modified alpha functions and acentric factor redefined at Tr = 0.6. The mutual diffusivities can be determined once the computed parameters, including oil swelling factors and gas composition, match the measured ones. Due to solvent dissolution, heavy oil is found to swell faster initially as both heat and mass transfer contribute to the oil swelling. After the system temperature is stabilized, the volume of the diluted oil expands slowly as a result of the ongoing mass transfer. The extraction process was evident during the experiments with the presence of oil components in the collected gas samples, and it was found to be stronger at a higher temperature. The diffusivities determined using different alpha functions in the PR EOS are similar, but the newly defined acentric factor with the optimum alpha function minimizes the deviation between the measured and computed parameters. In addition to new experimental data, the bidirectional mass transfer between solvent and heavy oil has been quantified for the first time by coupling heat transfer.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".