Expanded Fluid Based Viscosity Correlation Applied with Peng-Robinson Equation of State
Notice bibliographique
Résumé
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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Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».