Bibliographic record
Abstract
Abstract Vapor Extraction (VAPEX) is a promising technique for the recovery of heavy oils and bitumen in reservoirs where thermal methods cannot be applied. In this work, the effects of dispersion and viscosity variation in the boundary layer of VAPEX on natural convection are studied. Natural convection may happen in VAPEX, if the solvents that are used to decrease oil viscosity, increase its density. This can occur with carbon dioxide-based solvents. The reduction of the oil viscosity due to solvent dissolution can have a large impact on the onset of convection, by decreasing the critical Rayleigh number by up to two orders of magnitude. The analysis shows that the longitudinal Peclet number does not have a significant impact on natural convention; however, the transverse Peclet number is a crucial parameter in determining the critical Rayleigh number. The wave number increases with the Rayleigh number, but decreases with the transverse Peclet number. When the effect of viscosity is included in the analysis of boundary layer instability in VAPEX, natural convection may occur in highly permeable reservoirs, leading to a greater oil production rate. The current models that are used to predict the oil production rate in VAPEX do not consider this effect and underestimate the oil production rate in case natural convection occurs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.001 | 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".