Phase Behavior and Physical Property Modeling for Vapex Solvents: Propane, Carbon Dioxide, and Athabasca Bitumen
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Employing CO2 as the non-condensable gas in the Vapex process is an attractive option that could provide environmental benefits of CO2 sequestration along with improved Vapex performance. Mixtures of CO2 and a hydrocarbon such as propane allow the solvent to be tailored to different reservoir conditions. To test potential solvent mixtures, the phase behavior and physical properties measurements and modeling are required. We have previously reported on the phase behavior, viscosity and density of the CO2-propane-Athabasca Bitumen systems (Badamchi-Zadeh et al., 2009a,b). These results confirmed the ability of carbon dioxide and propane mixtures to sufficiently reduce Athabasca bitumen viscosity. In this study, an oil characterization and equation of state model are developed to describe the phase behaviour of mixtures of carbon dioxide, propane, and Athabasca bitumen. The model is tuned to fit the experimental phase behaviour data for binary and ternary mixtures of these components. Solubility data for carbon dioxide and Athabasca bitumen reported by Svrcek and Mehrotra 1982 are also used. It was found that two parameter cubic equation of state would require a third parameter (i.e. volume-shift) to better predict liquid density. The volume shift parameter was adjusted to improve cubic equation of state calculated liquid density against experimental data. Pederson (1987) viscosity correlation coefficients were modified to improve liquid viscosity prediction for propane, carbon dioxide, and bitumen mixtures.
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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 it