Quantification of Gas Exsolution Dynamics for Solvent-Heavy Oil Systems Under Reservoir Conditions
Bibliographic record
Abstract
Experimental and theoretical techniques have been developed to quantify foamy oil behaviour of solvent-heavy oil systems at bubble level during a gas exsolution process. During constant composition expansion (CCE) tests, we artificially induced foamy oil dynamics for solvent-heavy oil systems by gradually reducing pressure and recorded the changed pressures and volumes in an isolated PVT setup at a given temperature. By discretizing gas bubbles on the basis of the classical nucleation theory, we theoretically integrated the population balance equation (PBE), Fick’s law, and the Peng–Robinson equation of state (PR EOS) to reproduce the experimental measurements. Pseudo-bubblepoint pressure for a given solvent-heavy oil system can be increased with either a lower pressure depletion rate or a higher temperature, during which gas bubble growth is facilitated with a reduction in viscosity and/or an increase in solvent concentration, but gas bubble nucleation and mitigation is hindered with an increase in solvent concentration. Compared to CO2, CH4 is found to yield stronger and more stable foamy oil, indicating that foamy oil is more stable with a larger amount of dispersed gas bubbles at lower temperatures. Using the PR EOS together with the modified alpha functions at Tr = 0.7 and Tr = 0.6, the absolute average relative deviation (AARD) is reduced from 4.58% to 2.24% with respect to the predicted pseudo-bubblepoint pressures.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".