Bibliographic record
Abstract
Vapor extraction (VAPEX) is considered a promising alternative to the SAGD process to recover Alberta's heavy oil and bitumen resources. In the past, researchers have found that the main drawback of the VAPEX process is its slow oil production rate due to the inherent mechanism of relatively slower mass transfer compared to heat conduction and convection. Warm VAPEX, which combines the effect of both heating and solvent dilution, can increase the oil production rate significantly. In this study, the feasibility of injecting dimethyl ether (DME) to extract Athabasca bitumen is investigated through numerical simulations. A synthetic reservoir model with fine grids was developed to investigate the displacement mechanisms in a hot DME VAPEX process. Thermal dynamic properties of a bitumen-DME-water system were modelled and validated with experimental measurements. The performance of hot DME VAPEX is compared with a SAGD process in terms of an oil rate, cumulative oil production and energy input. Simulation results indicate that the oil rate of warm DME VAPEX is comparable to SAGD, and the energy consumption is dramatically reduced. Subsequently, a sensitivity analysis is conducted to examine the effect of various parameters on the overall performance of DME-based warm VAPEX. Injecting DME at higher temperatures is effective in reducing the solvent-oil ratio. Oil production can be significantly promoted by increasing the injection pressure. The original reservoir water saturation also has a significant impact on the performance of warm DME VAPEX. For higher water saturation reservoirs, oil production is enhanced, but more DME is trapped in the water phase.
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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".