The Role of Diffusion and Convective Dispersion in Vapour Extraction Process
Bibliographic record
Abstract
Abstract Vapour extraction (VAPEX) is a potentially economic process for the recovery of heavy oil and bitumen in Canadian reservoirs, as all of the injected solvent is effectively delivered to the zone of interest. In addition, the process has the potential to sequester greenhouse gases, and is capable of in situ upgrading of heavy oil. The research described in this paper was undertaken to identify the main processes governing the interfacial mass transfer of solvent into bitumen. A number of experiments were carried out in a Hele-Shaw cell, and the results were incorporated into a predictive model. Good agreement between theory and experiment was found when dispersion effects were incorporated into a mass transfer model of the process at identical values of Peclet number. Introduction Vapour extraction (VAPEX) is an alternative method for recovery of heavy oil and bitumen. This technique, which involves a solvent-leaching gravity drainage mechanism, reduces the viscosity of heavy oil by dissolution of a vapourized solvent into the bitumen. VAPEX has recently received a lot of attention from industry, as a promising means for recovery of heavy oil deposits in Canada. The primary drive for this interest is the potential economic attractiveness of the process in comparison to other heavy oil recovery techniques. Unlike steam injection, which is associated with significant heat losses to the media surrounding the wellbore and reservoir, all of the injected solvent by VAPEX is effectively delivered to the zone of interest. This process also appears to provide an alternative method for potential sequestration of greenhouse gases, particularly in regions at close proximity to power plants of northern Alberta. Moreover, experimental work has proved that VAPEX is capable of in situ upgrading of the heavy oil, due to the stripping phenomenon associated with scavenging the lighter end hydrocarbons by the flowing solvent(1). Therefore the costs associated with treatment and processing of produced oil by VAPEX is considerably less than that for other heavy oil production schemes. The prime objective of this paper is to identify the main processes governing the interfacial mass transfer of solvent into bitumen and incorporate those mechanisms into a reliable, predictive model. The estimated recovery rates for laboratory experiments in a Hele-Shaw cell appears to be well within the range of drainage rates predicted by molecular diffusion-based models(1). However, subsequent experiments in sand-packed porous media resulted in drainage rates, considerably higher than predicted values from analytical models(1, 3-5). Earlier researchers had suggested several different factors that might potentially enhance the mass transfer of solvent into the bitumen in the VAPEX process(5). However, no systematic investigation was carried out to understand and/or verify the viability of either of their proposed enhancement mechanisms. In order to have a better understanding of the dispersion and diffusion mechanisms of mass transfer for VAPEX, we began our experiments in a Hele-Shaw cell. Further experiments will be conducted in porous media, once the questions surrounding the process in a Hele-Shaw cell are addressed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".