Prediction of the Viscosity of Solvent Diluted Live Bitumen at Temperatures up to 175°C
Bibliographic record
Abstract
Abstract Accurate predictions of heavy oil and bitumen viscosity as a function of temperature, pressure, and composition are required for the design of thermal and solvent based recovery methods. In this case study, the applicability of the recently developed Expanded Fluid viscosity correlation is tested on measured viscosities of diluted dead and live bitumen at temperatures from 20 to 175°C and pressures up to 10 MPa. The data were collected for: 1) an Alberta bitumen, 2) a condensate, 3) diluted bitumen with 3, 6 and 30 wt% condensate, 4) live bitumen, and 5) diluted live bitumen with 3 and 5.9 wt% condensate. The live oil viscosity was 820 mPa.s at 50°C and 2.5 MPa compared with a dead oil viscosity of 3180 mPa.s. The Expanded Fluid (EF) viscosity correlation relates viscosity to density at any given pressure and temperature; it requires three parameters for each fluid. In this study, the dead bitumen and the condensate solvent are treated as single components and the viscosity correlation parameters are determined by fitting viscosity data. The parameters for the solution gas, live bitumen, and the bitumen-solvent mixtures were determined from mass based mixing rules. The parameters for the pure components that made up the solution gas were previously determined. The correlation was fitted to dead Alberta bitumen and the condensate with average relative deviations of 11% and 1.5%, respectively. The viscosity of the live bitumen was predicted to within 20% and 28% of the measured value based on measured and calculated mixture densities, respectively. Diluting the live and dead bitumen with 3 to 30 wt% solvent reduced the viscosity by one to three orders of magnitude and the viscosities were predicted with an average relative deviation under 16 and 37% based on measured and calculated mixture densities, respectively.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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".