Physics‐based proxy modelling of solvent transport in VAPEX process
Bibliographic record
Abstract
VAPEX (vapour extraction) is a non‐thermal process that has significant potential to provide a more environmentally friendly and energy‐efficient alternative to steam injection. While numerical modelling techniques are available for simulating mass transfer in VAPEX process, computational constraints often preclude detailed numerical solution of the flow and transport differential equations, as is often implemented in traditional flow simulators. Efficient alternatives that are based on analytical solutions could be employed to assess the transport physics. In this paper, a novel physical‐based proxy is developed to model solvent transport in VAPEX at isothermal conditions, in a way analogous to the SAGD model described by Butler (Butler, J. Can. Petrol. Technol. 1985, 24, 42). Detailed analytical formulations are derived and implemented in a calculation procedure to advance the solvent–bitumen interface and to estimate producing oil rate with time. In our approach, solvent concentration and intrinsic diffusion coefficient are introduced in the model instead of temperature and thermal diffusivity in SAGD. A new mass penetration parameter is introduced and its change with time is modelled. Growth of solvent chamber and oil drainage rate predicted from the proxy model are in good agreement with Hele‐Shaw experimental data available in the literature. Results predicted by proxy model also match well with scaled‐up flow rates for a field case described by Das and Butler (Das and Butler, J. Can. Petrol. Technol. 1994, 33, 39).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".