Simple Calculation of the Effect of Volatile Solvents and Dissolved Gases on the Viscosity of Heavy Oils and Bitumens
Bibliographic record
Abstract
Abstract A new method is proposed for simple and reliable viscosity calculations for mixtures of heavy oils or bitumens with light solvents. This method has three characteristics that give it an advantage over current approaches. First, it is simple to implement. Second, it faithfully portrays the viscosity of mixtures of Newtonian fluids up to at least 190°C. Third, and most importantly, it makes use of the recent discovery that, for any given heavy oil, all light solvents can be represented at low concentrations by the same mole-fraction-weighted form of the Arrhenius equation. For high concentrations, an additional, empirical power-law term, whose exponent was determined from new viscosity data for toluene–heavy oil mixtures, was developed. The average absolute relative deviation (AARD) between 307 experimental viscosity measurements and the new equation was 20.1%. The data set included mixture viscosities from 1 to 440,000 mPa·s, at temperatures from 12 to 190°C. The largest disagreements were -63% and 101% of the measured values. It is suggested that a substantial portion of the observed disagreements arose from combined errors in the experimental measurement of viscosities, solvent compositions, and average molecular weights of the oils. It was found that the one key constant for describing concentration effects could be correlated reasonably well with the viscosity of the dead oil. When this constant was calculated from the resulting correlation, the AARD for the initial data set increased by only a modest amount to 27.7%. The disagreements were somewhat larger when the new method was used to make pure predictions of two published data sets. Accordingly, it was concluded that the new approach can be used to make reasonable predictions (possibly within a factor of 2) of solvent–oil mixture viscosities simply from accurate knowledge of the viscosities and molecular weights of the dead oil and solvent. However, substantially better predictions may be obtained for any light solvent if the above information is supplemented with even relatively inexpensive laboratory data taken with a lower-volatility solvent such as toluene.
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How this classification was reachedexpand
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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".