The Effect of Solvents on the Viscosity of an Alberta Bitumen at In Situ Thermal Process Conditions
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Bibliographic record
Abstract
Abstract The design and optimization of solvent assisted thermal recovery processes for heavy oil and bitumen require accurate predictions of viscosity as a function of temperature, pressure, and composition. In this case study, the performance of the Expanded Fluid (EF) viscosity model is tested on viscosity data for an Alberta bitumen diluted with carbon dioxide (5.2 wt%), ethane (5.1 wt%), propane (7.6 and 16 wt%), n-butane (14.5 wt%), n-pentane (15 and 30 wt%) and n-heptane (15 and 30 wt%) at temperatures from 20 to 175°C and pressures up to 10 MPa. The main input to the EF model is the density of the fluid and densities were measured at the same conditions as the viscosity measurements. The viscosity of the bitumen was fitted with average absolute relative deviation (AARD) of 8%. The viscosities of the diluted bitumen mixtures were predicted without tuning with an overall AARD of 17% when using measured densities as an input. The viscosity predictions were improved to an AARD of 7% using generalized viscosity binary interaction parameters. When using densities calculated with an excess volume based mixing rule, the viscosity predictions were slightly more deviated with an overall AARD of 10%. The EF model predictions were used to evaluate the effectiveness of n-alkane solvents in reducing bitumen viscosity at in situ steam-solvent process conditions. The solubility of the solvent in bitumen was found to be the main factor controlling the mixture viscosity. The less volatile the solvent is, the greater is the viscosity reduction at a given pressure and temperature. As the process temperature increases, the greater is the viscosity reduction from a given solvent due to increased solubility at higher steam saturation pressures.
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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) | 0.001 | 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 it