Prediction of Viscosity for Characterized Oils and Their Fractions Using the Expanded Fluid Model
Bibliographic record
Abstract
A methodology has been developed to predict the viscosity of crude oils and their fractions from a distillation-based oil characterization. The maltenes were characterized as a set of pseudo-components with properties determined from established generalized correlations. The asphaltene fraction was characterized as a single component and its properties were measured. The viscosities of the pseudo-components, asphaltenes, whole oils, and their fractions were determined with the Expanded Fluid (EF) viscosity model. The inputs for the model are the pressure, the density of the fluid at a given pressure and temperature, the dilute gas viscosity calculated from established generalized correlations, and three fluid-specific parameters: c 2, ρ s °, and c 3 . Densities were calculated using the modified Rackett correlation with the Tait-COSTALD compressibility correction. The c 3 parameter was determined from a previously developed correlation. New correlations were developed for the c 2 and ρ s ° parameters of the maltene pseudo-components, as a function of their boiling point and specific gravity. The parameters for the asphaltene fraction were estimated based on the measured viscosity of molten asphaltenes. The EF parameters for the whole oil, or any oil fraction, were determined with mass-based mixing rules and binary interaction parameters, calculated from a previously developed correlation. To develop and test the proposed approach, density and viscosity data were collected for 40 distillation cuts from 6 oils, 7 maltenes, 2 asphaltenes, 3 partially deasphalted oils, and 14 dead oils. Using this model to predict crude oil viscosity under any conditions requires the distillation assay data, the asphaltene mass content, and the specific gravity and molecular weight of the oil. The approach was tested on a development and test dataset of 4 crude oils (this study) and an independent test dataset of 4 oils from the literature with overall average absolute relative deviation (AARD) values of 41% and 43%, respectively. Single multiplier tuning of the c 2 parameter to one viscosity data point halved the error. Tuning both the c 2 and ρ s ° parameters using two viscosity data points reduced the AARD to <8% in both cases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".