Modelling of dynamic mass transfer in a vapour extraction heavy oil recovery process
Bibliographic record
Abstract
ABSTRACT Viscosity reduction through solvent dissolution into heavy oil is one of the most important recovery mechanisms of a vapour extraction (VAPEX) process. Existing analytical models can neither accurately describe the mass transfer between solvent vapour and heavy oil nor predict the solvent chamber evolution. Simulation models are confounded by numerical dispersion and have difficulty in accurately characterizing fluid properties in VAPEX. This study first develops a mass transfer model to describe a dynamic heavy oil‐solvent mixing process. This model is then incorporated into a VAPEX model to estimate solvent chamber development and an oil production rate. Diffusivity is determined through history matching theoretically calculated and experimentally measured cumulative oil production data. It is found that both constant and variable diffusivities can achieve an excellent match in cumulative oil production data. However, their respective characterization of the fluid properties in the VAPEX transition zone is very different. This study also proposes a method to convert constant diffusivity into its equivalent variable diffusivity for VAPEX by using some regressed correlations. Moreover, the back‐calculated effective diffusivity is found to be about 10–30 times of the corresponding molecular diffusivity measured in the laboratory.
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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".