Expanded Fluid Based Viscosity Correlation Applied with Peng-Robinson Equation of State
Bibliographic record
Abstract
Abstract The viscosity of pure hydrocarbons was correlated using a simple function based on the fluid density. The correlation has three adjustable parameters, a compressed state density, ρs °, an empirical parameter, c2, that scales the viscosity response to fluid expansion, and another empirical parameter, c3, used to tune at pressures above 10 MPa. The only input to the correlation is the fluid density and, in the previous work, measured densities were employed. The correlation was tested on experimental viscosities for 39 pure hydrocarbons including n-alkanes, branched alkanes, alkenes, cyclics, and aromatics as well as heavy hydrocarbons such as mineral oils, binary mixtures of pure hydrocarbons, and heavy oils diluted with solvent. In this study, the correlation is retuned to use the densities predicted from the Advanced Peng Robinson equation of state. The method provides a single framework for liquid and vapour phases, is simple to implement, and is very fast computationally making it ideal for incorporation into process and reservoir simulators. Introduction The calculation of viscosities is an important part of process and reservoir simulation where the correct calculation of pressure drops and heat transfer coefficients is paramount. When applied to computer simulation problems, correlations should have the following properties:a small number of adjustable parameters to ensure a maximum of physical significance and predictable extrapolation behavior;continuity of vapor and liquid values across the critical point of the solution;easily determined parameters from incomplete or estimated data;speed. As well many existing viscosity correlations were developed and tested on pure hydrocarbons and light petroleum fluids. However, with the increasing development of heavy oil and bitumen resources, a reliable viscosity correlation for heavy petroleum is also required. Both thermal and solvent based processes have been used or are being considered to recover, process, and transport heavy oils. Hence, a viscosity correlation is required that can extend to light hydrocarbons, conventional crude oil, heavy oils and their mixtures as a function of temperature and pressure. Our goal was to develop a simple correlation based on the fact that viscosities correspond to densities, and use this idea for the correlation of a large amount of experimental data. The constraints were:to develop model parameters that have a simple physical interpretation;to have simple mixing rules that provide reasonable predictive capabilities even without adjustable interaction parameters;to be computationally efficient and directly applicable to commercial simulators. A second goal was to ensure that the correlation could be used to estimate the viscosities of heavy oils, mixtures of heavy oils, and mixtures of heavy oils and solvents. In previous work (1), the correlation was developed using measured densities. In this study, the correlation is retuned to use densities predicted with the Advanced Peng Robinson Equation of State (APR EoS) (2). Correlation using Measured Densities The general principle behind the correlation is very simple: as a fluid expands there is greater distance between molecules and its fluidity increases.
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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 | 0.001 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".