Extensive experimental investigation of the effect of drainage height and solvent type on the stabilized drainage rate in vapour extraction (VAPEX) process
Bibliographic record
Abstract
The low cost of the injected solvent, which can be also recovered and recycled, and the applicability of VAPEX technique in thin reservoirs are among the main advantages of VAPEX process compared to thermal heavy oil recovery techniques. In this research, an extensive experimental investigation is carried out to first evaluate the technical feasibility of utilization of various solvents for VAPEX process. Then the effect of drainage height on the stabilized drainage rate in VAPEX process was studied by conducting series of experiments in two large-scale 2D VAPEX models of 24.5 cm and 47.5 cm heights. Both models were packed with low permeability Ottawa sand (#530) and saturated with a heavy oil sample from Saskatchewan heavy oil reservoirs with viscosity of 5650 mPa s. Propane, butane, methane, carbon dioxide , propane/carbon dioxide (70%/30%) and propane/methane (70%/30%) were considered as respective solvents for the experiments, and a total of twelve VAPEX tests were carried out. Moreover, separate experiments were carried out at the end of each VAPEX experiment to measure the asphaltene precipitation at various locations of the VAPEX models. It was found that injecting propane would result in the highest drainage rate and oil recovery factor. Further analysis of results showed stabilized drainage rate significantly increased in the larger physical model.
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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 | 0.001 | 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".