A Stochastic Approach to Extract Vapex Related Dispersion Coefficients from Magnetic Resonance Images
Bibliographic record
Abstract
Summary In vapor extraction (Vapex) process, the dispersional mixing between injected solvent vapor (propane) and in-situ bitumen occurs along the oil-solvent interface during the oil drainage process. The solvent dispersion coefficient is a key parameter that governs the oil dilution efficiency as well as the rate of production. To predict the field performance of the Vapex process it is vital to accurately estimate the value of the dispersion coefficient at the field conditions of interest. Currently, there are no factual data available in the literature and there is no proven empirical methodology for estimating the dispersion coefficients that would be pertinent to the Vapex process. Recently, the Magnetic Resonance Imaging (MRI) tools have been used to gain insights into the Vapex process. The MRI technique can generate 2-dimensional (2-D) images during the progress of a laboratory-scale Vapex experiment. Both the original bitumen and the solvent vapor are virtually invisible in these MRI generated 2-D images. However, the propane saturated bitumen is clearly visible and in the diluted oil zone, the signal intensity is a function of the dissolved solvent concentration. This paper describes a new technique to extract the net dispersion coefficients pertinent to the Vapex process from 2D MRI images captured during a test. A new mathematical model has been developed for analyzing such 2-D images. The model portrays the unique context of mass transfer mechanisms and the interface propagation in the Vapex process. The technique has been used on a previously published MRI image and found to be very effective and straightforward.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".