Enhanced Vapour Extraction (VAPEX) of Heavy Oils through Foamy Oil Flow and Viscous Fingering
Bibliographic record
Abstract
Abstract Solvent vapour extraction (VAPEX) has shown great potential to recover heavy oil reserves. However, the application of the VAPEX technique is limited by its low oil production rate, which is caused by the slow mass transfer and inefficient gravity drainage. Hence, increasing the heavy oil–solvent mixing process and introducing more production mechanisms are imperative to enhance the performance of VAPEX. This paper proposes a new process, namely enhanced VAPEX, in which the solvent-chamber pressure is dynamically operated rather than constantly maintained throughout the production process. During the enhanced VAPEX process, pressure in the solvent chamber is cyclically depleted to induced foamy oil flow, which not only adds oil production but also increases contact area. In addition, a high-pressure non-condensable gas is applied to induce fingering phenomenon, which enlarges the heavy oil–solvent contact area. The enhanced VAPEX process is experimentally evaluated in this study. It is found that gas fingers can penetrate deep into the oil zone. In addition, foamy oil flow not only increases oil production but also improves mass transfer. Experimental results show that the oil production rate of the conventional VAPEX process was increased by 182% by the enhanced VAPEX process.
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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 | 0.000 |
| 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".