Performance Enhancement of Vapex by Varying the Propane Injection Pressure with Time
Bibliographic record
Abstract
Vapex or vapor extraction is an emerging green technology for heavy oil recovery. However, the oil production rates with Vapex are lower than those with the conventional recovery processes. This work aims at enhancing the oil production rates by investigating the effect of varying the injection pressure of solvent propane with time. For this purpose, experiments were designed and performed by injecting pure propane at injection pressures of 482.6, 551.6, 620.5, and 689.5 kPa and 21 °C into lab-scale physical models of heavy oil reservoirs. The physical models were packed with a porous medium and saturated with heavy oil. Three different permeabilities of the porous medium were used with heavy oils of two different viscosities and bed heights. The experiments were performed using different policies of solvent injection pressure versus time. Pressure variations were introduced by sudden release and re-injection of the solvent gas. In comparison to constant injection pressure, the pressure pulsing enhanced the oil production rate by 20–30%.
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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".