Molecular Diffusion and Dispersion Coefficient in a Propane-Bitumen System: Case of Vapour Extraction (VAPEX) Process
Bibliographic record
Abstract
Abstract The solvent based recovery methods have recently attracted considerable attention due to environmental and energy concerns associated with thermal recovery methods. However, knowledge about molecular diffusion and mechanical dispersion as two key parameters to characterize these processes is limited. To find out the contribution of convective dispersion in enhanced production rates in VAPEX, three experiment sets were conducted for a system of propane and Athabasca bitumen. In the first set, molecular diffusion coefficient of propane in Athabasca bitumen was determined through a constant-pressure technique. A numerical technique was developed which also accounts for significant swelling of bitumen due to propane dissolution. The Levenberg-Marquardt method was utilized to estimate the molecular diffusivity through an inverse approach. In the other experimental set, a physical sand pack model was used to run the VAPEX experiments in the same temperature and pressure. The obtained dead oil production rate values were introduced into VAPEX analytical model. It allowed us to back-calculate the value of dimensionless number Ns. Finally, a set of PVT measurements were conducted to determine the viscosity and density of diluted bitumen for different values of propane concentration. This enabled us to find the dispersion coefficient from the definition of Ns term. Molecular diffusion and convective dispersion are two important parameters for characterization of processes like VAPEX, ES-SAGD and other hybrid thermal solvent scenarios. Accurate estimation of these parameters improves predictions of compositional reservoir simulators. Obtaining propane molecular diffusivity into the bitumen and comparing it with our VAPEX back-calculated dispersion coefficients, would allow us to evaluate contribution of the convective dispersion to recovery rate enhancement in VAPEX experiments.
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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".