A NEW MATHEMATICAL MODEL FOR THE SOLVENT CHAMBER EVOLUTION IN THE VAPOR EXTRACTION PROCESS
Bibliographic record
Abstract
Extensive physical modeling of vapor extraction (VAPEX) has been studied in the past two decades, yet the theoretical modeling of VAPEX has not gained much progress. The major VAPEX mechanisms, i.e., oil viscosity reduction through solvent dissolution and gravity drainage, occur mainly in a thin solvent − heavy oil transition zone. Therefore, modeling ofthe transition zone is the key to model the VAPEX process. Current analytical models are based on some potentially unreliable assumptions for the transition zone, such as steady-state mass transfer and constant boundary moving velocity. Numerical models cannot fully capture the physical features because the gridblock is usually much larger than the transition-zone thickness. This paper develops a new mathematical model for the VAPEX transition zone that is simplified as a piecewise linear profile and updated step by step. In each step, first, a solvent concentration distribution is calculated by using Fick's second law. Then the oil drainage velocity is computed by using Darcy's law. Finally, the momentary boundary moving velocity is estimated by using a mass balance equation. The VAPEX model is not only able to describe the evolution ofthe solvent chamber, but also characterize the dynamic oil properties across the transition zone. Compared with numerical simulation, this new model shows more sensitivity to the diffusion coefficient.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".