Viscosity Prediction for Solvent-Diluted Live Bitumen and Heavy Oil at Temperatures Up to 175-deg-C
Bibliographic record
Abstract
Summary Accurate predictions of heavy-oil and bitumen viscosity as a function oftemperature, pressure, and composition are required for the design of thermaland solvent-based recovery methods. In this case study, the applicability ofthe recently developed Expanded Fluid (EF) viscosity model is tested onmeasured viscosities of diluted dead and live heavy oil and bitumen attemperatures from 20 to 175°C and pressures up to 10 MPa. Density and viscositydata were collected for a condensate solvent, dead (gas-free) bitumen, and deadheavy oil from western Canada, and for the corresponding live oils and dilutedmixtures of the dead and live oils with condensate solvent. Solubility,density, and viscosity data for heavy oil saturated with carbon dioxide(CO2) were obtained from the literature. The model was fitted to thedata of the dead oils and the condensate with average relative deviations lessthan 11%. The viscosity of the live bitumen and heavy oil was then predicted towithin 21 and 31% of the measured value on the basis of measured and calculatedlive-oil densities, respectively. Diluting the live and dead bitumen with 3 to30 wt% condensate or carbon dioxide reduced the viscosity by one to threeorders of magnitude, and the viscosities were predicted with an averagerelative deviation less than 16 and 24% on the basis of measured and calculatedmixture 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 | 0.001 | 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".