Vapor–Liquid Equilibrium of Bitumen–Ethane Mixtures for Three Athabasca Bitumen Samples
Bibliographic record
Abstract
Steam–solvent coinjection processes have received more attention in the past decade due to the environmental impact of the pure steam injection process. Increasingly restrictive environmental regulations including carbon tax have served to push the industry to consider coinjection processes (steam + solvent) to reduce greenhouse gas emissions. Understanding the phase behavior of solvent/bitumen mixtures is critical for feasibility studies as well as the design and implementation of a successful coinjection process. This study presents the vapor–liquid equilibria for bitumen/ethane mixtures and their applications for bitumen recovery processes. Experiments were conducted for temperatures up to 190 °C and pressures up to 10 MPa to simulate the conditions of in situ steam processes. The results of our vapor–liquid equilibrium experiments include solubility, viscosity, and density measurements of the saturated liquid phase, k- values, and gas oil ratio. Increasing the temperature from 50 to 150 °C resulted in a significant drop in ethane solubility in the bitumen. However, increasing the temperature from 150 to 190 °C had a negligible impact on solubility. As a result, viscosity reduction is much lower at higher temperatures. The viscosity of ethane-saturated bitumen changed linearly with pressure at three temperatures (100, 150, and 190 °C) in a semilog plot. A nonlinear trend was recorded at high pressure and 50 °C with liquid–liquid behavior characteristics. The solubility of ethane is in the same range for the three bitumen samples used in this study which indicates that this characteristic of Athabasca bitumen is not dependent on geographical location.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".