Dispersion Determination in VAPEX: Experimental Design, Modelling and Simulation
Bibliographic record
Abstract
The heavy oil and bitumen reservoirs of Canada are one of the largest hydrocarbon sources in the world. Vapor extraction of heavy oil, or VAPEX, has emerged as a very promising recovery process since its inception in 1991. The principal reason is the environmental friendliness of VAPEX together with its cost-effective nature vis à vis other recovery processes. In this work, a review has been done on various factors affecting VAPEX process. Also, a lab-scale VAPEX experimental setup is designed to determine the dispersion coefficients of solvent gases in heavy oil and bitumen. Further, a mathematical model is developed based on earlier reported rectangular physical model of homogenous porous medium saturated with heavy oil and bitumen. The developed mathematical model is simulated to determine gas dispersion along with solubility during the vapor extraction of live oil from a laboratory scale physical model. At a given temperature and pressure, the block is initially exposed on its side to a solvent gas, which diffuses into the medium and gets absorbed. The absorption of gas reduces the viscosity of heavy oil and bitumen causing it to drain under gravity. The low-viscosity “live oil” is produced at the bottom of the porous block. The production of live oil with time is accompanied by the shrinkage of block as well as its increased exposure to gas from top. These phenomena of VAPEX are described by the mathematical model, which is used to calculate live oil production with various values of gas solubility and dispersion. Their optimal values are determined for the vapor extraction of Cold Lake bitumen with butane by matching the calculated live oil production with its experimental values published earlier.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".