Extension of the Expanded Fluid Viscosity Model to Characterized Oils
Bibliographic record
Abstract
The Expanded Fluid (EF) viscosity model for Newtonian fluids is extended to crude oils characterized as mixtures of defined components and pseudo-components. The EF models take the fluid density, dilute gas viscosity, pressure, and fluid composition as inputs and requires three fluid-specific parameters, c 2, c 3, and ρ s o, for the fluid or its components. Generally, experimental viscosity data are required to determine these values for each component. In this study, an internally consistent estimation method was developed to predict the fluid-specific parameters of the model for hydrocarbons when no experimental viscosity data are available. The method uses n -paraffins as the reference system and correlates the fluid-specific parameters for hydrocarbons as departures from the reference system. The method was evaluated against viscosity data of over 250 pure hydrocarbon compounds and petroleum distillation cuts. The model predictions were within the same order of magnitude of the measurements, with an overall average absolute relative deviation of 31%. The method was then used to calculate the correlation parameters for the pseudo-components of nine dead and live oils characterized on the basis of their gas chromatography (GC) assays. The viscosities of the crude oils were predicted within a factor of 3 of the measured values using the measured density of the oils as the input. The applicability of the EF model was also demonstrated using the densities determined with the Peng–Robinson equation of state. A simple method was proposed to tune the model to available viscosity data using a single multiplier to the c 2 parameter (and also to c 3 and ρ s o if necessary) of the pseudo-components. Single-parameter tuning of the model improved the viscosity prediction for the characterized oils to within 30% of the measured values.
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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 | 0.000 |
| 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) | 0.000 | 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".