Equation of State Coupled Predictive Viscosity Model for Bitumen Solvent-Thermal Recovery
Bibliographic record
Abstract
Abstract Exponential and polynomial viscosity correlations have been widely applied to model crude viscosities with temperature. These simple correlations are difficult to be applied to predict the viscosity of different solvent-diluted bitumen systems over a wide range of solvent composition. The Expanded Fluid viscosity model consisting of density as an input parameter can be coupled with an Equation of State in a compositional and thermal reservoir simulator. However, the accurate prediction of density using an EoS is the prerequisite to apply this viscosity model. In this work, the expanded fluid theory was coupled with the simplified PC-SAFT EoS (Perturbed-Chain Statistical Associating Fluid Theory) to predict and correlate the rheology behaviour of bitumen/solvent systems. Athabasca and Peace River Bitumen was characterized using a proposed eight-pseudocomponent characterization method for PC-SAFT, which simply required distillation and molar mass data. The obtained density was then input into the viscosity theory to model viscosity. Viscosity predictions were obtained using zero viscosity binary interaction coefficients, whereas pressure-dependent and temperature-dependent viscosity binary interaction coefficients were adjusted to improve the effectiveness of mixing rules. In the case of Athabasca Bitumen with CH4, C2H6, and CO2, the correlated solubility and density Average Absolute Relative Deviations (AARDs) were within 6.6 % and 2.3 %, respectively. Viscosity AARDs by prediction were within 55.4 %; whereas the AARDs were reduced within 13.5 % using pressure-dependent viscosity binary interaction coefficients. In the case of Peace River Bitumen with C2H6, C3H8, n-C4H10, n-C5H12, the predicted density AARDs were within 0.7 %. Viscosity AARDs obtained by prediction were within 24.9 %, and they were reduced within 8.4 % once using temperature-dependent viscosity binary interaction coefficients.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".