Wanted Dead or Live: Crude-Cocktail Viscosity--A Pseudocomponent Method to Predict the Viscosity of Dead Oils, Live Oils, and Mixtures
Bibliographic record
Abstract
Summary A broadly applicable methodology is presented to reliably predict crude-oil-liquid viscosity from a gas-chromatographic (GC)-assay composition only (C30+ is recommended). The viscosity model employs a Walther-type correlation of double-log viscosity with log temperature to predict the viscosity of dead and live crude oils and mixtures. The model has three parameters: the slope and intercept of the Walther plot and a viscosibility factor to account for pressure effects. Simple mass-based mixing rules are applied on these three parameters to obtain mixture viscosity. The three parameters were correlated to component molecular weight (MW); therefore, a gas-chromatographic assay is the only required input apart from the temperature and pressure. The methodology was developed from a western Canadian (WC) data set of two bitumens, one heavy oil, and one condensate, and then tested on an independent data set of 10 conventional and heavy crude oils from the Gulf of Mexico, the Middle East, Asia, and Europe. The model provides untuned viscosity predictions within a factor of two of the measured values for dead and live crude oils ranging in viscosity from 0.5 to 500 000 mPa·s. A single multiplier is used to tune the model. Models tuned to dead-oil data predict live-oil viscosities and those of mixtures of oils with solvents to within 30% of the measured values. Models tuned to the viscosity at the saturation pressure predict the effect of temperature and pressure to within 20% of the measured values. The method retains its accuracy when components are lumped into a few pseudocomponents and is suited ideally for use in simulators for accurate liquid-phase viscosity predictions over a wide range of compositions, pressures, and temperatures. It would be necessary to include the proposed mixing rules in numerical simulators. An additional advantage of the method is the reduction in viscosity measurements needed to construct an accurate viscosity model.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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 | 0.002 |
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".