Effects of Fracture Parameters on VAPEX Performance: A Numerical and Experimental Approach Utilizing Reservoir-On-The-Chip
Bibliographic record
Abstract
The present research carries out an in-detail study of the VAPEX process as one of the most recent solvent-based heavy oil recovery techniques in fractured reservoirs to evaluate the effect of fracture parameters on process performance. To achieve this purpose, several fractured patterns with distinct features were designed and engraved on glass pieces to manufacture state-of-the-art microfluidic models mimicking a typical Canadian heavy oil reservoir. A heavy oil sample of viscosity 1514 cP was utilized during the conducted experiments with pure propane and pure carbon dioxide as the injection solvents. A thorough image analysis operation was carried out over the experimental models to determine heavy oil produced, residual oil saturation, ultimate recovery factors, and monitor solvent chamber expansion. Numerical simulations of the same experiments were carried out for history matching and predicting other designed scenarios. Error analysis revealed average absolute errors of below 8%, showing convincing precision. Together with the simulation outcomes, a comprehensive data bank was obtained from the 30 scenarios designed and 18 VAPEX experiments conducted. The effects of fracture orientation, length, width, intensity, and position on process performance were identified and numerically evaluated. It was observed that all fractures, regardless of their properties, enhanced heavy oil recovery in comparison to the base case (no fractures) scenario. Moreover, propane proved more efficient owing primarily to its higher solubility and effective dispersion. The highest recovery factor, 65.81%, was obtained when implementing two wide vertical fractures on either side of the well pair. Almost equal to that, 64.93% was the process efficiency by positioning two long horizontal fractures between the wells.
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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 | 0.000 | 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".