Mathermatical Modeling of Dynamic Mass Transfer in Cyclic Solvent Injection
Bibliographic record
Abstract
Abstract In a cyclic solvent injection (CSI) process, a solvent gas is cyclically injected, soaked, and released to produce heavy oil. Through this process the operating pressure is decreased and increased cyclically. This causes a dynamically changing pressure gradient across the solvent–oil mixing zone, leading to a convective mixing of solvent and oil in addition to molecular diffusion. This study aims at modeling the effect of pressure gradient-induced convection on the solvent–oil mixing process in the transition zone. A diffusion–convection model is developed to simulate the dynamic solvent–oil mass transfer in the transition zone during the CSI process. This model consists of two sub-models: a pressure model and a solvent concentration model. The pressure model considers the effect of a practical viscosity profile and the concentration model considers the effect of a dynamic convective mass transfer velocity. These two models are coupled through the convection velocity and oil viscosity across the transition zone. The entire model is semi-analytically solved, in which a piecewise linear approximation scheme is applied for the variable convection velocity and viscosity in the transition zone. Results show that during the solvent injection period, convection can significantly enhance the mixing of solvent and heavy oil. Larger solvent injection rates can be more efficient for heavy oil–solvent mass transfer. In addition, it is found that considerable solvent remains in the oil zone at the end of the production period, and this part of solvent is difficult to retrieve within a short time period.
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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".