Viscosity Modeling of Solvent-Heavy Oil/Bitumen Systems at High Pressures and Elevated Temperatures
Bibliographic record
Abstract
Abstract This work presents a novel framework for reproducing the measured viscosity of solvent-heavy oil/bitumen systems by integrating the Peng-Robinson equation of state (PR EOS) with binary interaction parameters (BIPs) and modified alpha functions. The oil samples are treated as either as a single pseudocomponent (PC) or multiple PCs. Also, the six most common mixing rules (i.e., Arrhenius’ mixing rule, Cragoe’s mixing rule, double-log mixing rule, Lobe’s mixing rule, power law mixing rule and Shu’s mixing rule) have been evaluated and compared. By adopting the effective density concept, the volume-based power law, weight-based power law, and weight-based Cragoe’s mixing rules reproduce the measured viscosity from 4.3–15000.0 mPa·s within the pressure and temperature range from 1.1 to 10.9 MPa and from 287.9 to 463.4 K. When utilizing one PC, the overall absolute average relative deviation (AARD) for viscosity prediction is found to be 16.0%, 16.5%, and 29.4% for the aforementioned mixing rules. When utilizing four PCs, the AARDs decrease to 13.9%, 14.8%, and 19.3% for the same mixing rules. It is evident that such framework has ability to predict solvent-heavy oil mixture viscosity reasonably accurate under various conditions, while it can be easily incorporated into any reservoir simulators within a given reservoir.
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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 | 0.000 | 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 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".