Measurement and Prediction of Volumetric Properties for Undersaturated Athabasca Bitumen
Bibliographic record
Abstract
The application of solvent injection for heavy oil and bitumen recovery processes requires the predictions of the performance for a field-scale design. This directly depends upon the variation of oil properties by solvent dilution. Thus, the comparison and evaluation of the solvent effects on the density and viscosity of oil are crucial. In this study, a constant number of solvent moles is dissolved in 1 mol of bitumen at a constant temperature and pressure and the thermophysical properties of mixtures are measured. Different solvents, methane, ethane, propane, butane, and carbon dioxide, are considered for the measurements. The measurements are taken at temperatures varying from 323 to 463 K, pressures of 4, 6, 8, and 10 MPa, and mixtures with different mole percentages of the solvents (5, 10, and 15). The results indicate that, at a constant temperature, pressure, and solvent mole fraction, the undersaturated carbon dioxide/bitumen mixture has the highest density and the undersaturated butane/bitumen mixture has the lowest density. Among the hydrocarbon gases, a general slight decreasing trend of undersaturated bitumen density with carbon number is observed. Finally, the mixture densities are predicted with an effective liquid density approach with a maximum average absolute relative deviation (AARD) of 0.17%.
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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 | 0.000 |
| 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 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".