Experimental Investigation of Oil Drainage Rates in the Vapex Process for Heavy Oil and Bitumen Reservoirs
Bibliographic record
Abstract
Summary Vapor extraction (Vapex) process is an emerging technology for viscous oil recovery that has gained much attention in the oil industry, as it appears to be superior to the currently used thermal based recovery methods. The process has potential to succeed even in some problematic scenarios, such as reservoirs with an overlying gas cap, bottom water table, high water saturation, low thermal conductivity, thin pay zone, etc. However, the oil production rates in the field as predicted by previous researchers are too low to make this process attractive for field implementation. Their predictions are based on the results from physical model experiments and the scaling-up method that hypothesizes that the oil recovery rate in the Vapex process should be proportional to the square root of the reservoir transmissibility. This scaling-up theory ignores the role of dispersional mixing between solvent vapors and in-situ oil during the gravity drainage process in porous media. The objective of this study was to reinvestigate the oil drainage rates and thereby examine the scale-up method in the Vapex process. An extensive experimental study was carried out with three different physical models of varying sizes that were partially scaled. These models were packed with the sand-grains of three different size distributions. The Vapex experiments were carried out to investigate the effects of laboratory model size and sand-grain size on the observed performance of the Vapex process. The results show that much higher oil rates in field processes are possible compared to those predicted by previous investigators based on the results from Hele-Shaw cell experiments and the available scale-up procedure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".