Comprehensive experimental study and numerical simulation of vapour extraction (VAPEX) process in heavy oil systems
Bibliographic record
Abstract
Abstract There are significant heavy oil and bitumen resources in Canada. Global energy demand is rising while environmental constraints make heavy oil recovery more challenging. Therefore, looking for an economically viable and environmentally‐friendly heavy oil recovery technique is essential. Recently, solvent‐based heavy oil recovery techniques (i.e., VAPEX) have attracted attention due to their economic and environmental advantages over thermal methods. In this research, an extensive experimental and numerical simulation study on the VAPEX technique was carried out to provide more in‐depth information about key parameters which affect the ultimate performance of the VAPEX process. For this purpose, VAPEX experiments were conducted in two large‐scale physical models and various solvents were utilized. PVT experiments were also carried out, and CMG's STARSTM was used for numerical simulation studies and to history‐match the experimental results. Image analysis of the VAPEX chamber evolution showed that the highest sweep efficiency was observed after injecting propane, followed by butane, a propane/carbon dioxide mixture, a propane/methane mixture, carbon dioxide, and methane. The experiments were simulated numerically, and satisfactory history‐matching results were achieved. The major difference between the experimental and simulation results was observed after the first breakthrough of the solvent. In addition, the results showed that injection and production well configurations significantly affected the recovery performance of the process. A longer distance between the injection and production wells alongside the drainage height will increase the production rate in VAPEX.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".