Prediction of the Liquid Viscosity of Characterized Crude Oils by Use of the Generalized Walther Model
Bibliographic record
Abstract
Summary A predictive but tunable model for the liquid viscosity of characterized crude oils was developed by use of the generalized Walther correlation (Walther 1931; Yarranton et al. 2013). The crude oils are each characterized into maltene pseudocomponents and a single C5-asphaltene component. The viscosity model requires two pseudocomponent parameters (A and B), two whole-oil parameters (δ1 and δ2), and binary-interaction parameters. The asphaltene parameters were determined experimentally and fixed for all cases. Correlations were developed for the maltene-pseudocomponent parameters and the binary-interaction parameters. The required data are the absolute temperature, pressure, the C5-asphaltene content, the specific gravity (SG) and molecular weight (MW) of the oil, and the boiling-point distribution of the maltenes. The SG and MW distributions of the maltenes are also required, but are generated from existing correlations. The proposed model predicted the viscosity of five western Canadian and two Colombian bitumens; three American, one Mexican, one Venezuelan, and one European heavy oil; and also a conventional oil from the Middle East with an overall average deviation of 57%. Tuning to a single viscosity data point with a single tuning parameter reduced the overall deviation to 8%, close to the 4% deviation obtained when the model was fitted to the data.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".