Phase Behavior Modelling of Oils in Terms of SARA Fractions
Notice bibliographique
Résumé
Abstract One of the key steps towards improving the predictability of air-injection-based processes relies on the development of accurate phase behavior models of the oil. Historically, for in-situ combustion (ISC) in heavy oils and bitumens, phase behavior was often ignored, as the physical aspects of the process (e.g. distillation) were not considered to be as significant as the oxidation reactions. However, this step is important for several reasons. First, the compositional model should reflect the phase behaviour of the original fluids. Second, reaction rates are dependent on the concentration of the reactants, which in turn are affected by the volatility of the components. This is particularly important for lighter oils (but not unimportant for heavier oils) where the phase equilibrium between the liquid and vapour can have a significant impact on the flammability range for vapour phase combustion at given temperature and pressure conditions. Finally, for the case of lighter oils, a good phase behaviour model is required to capture the compositional effects of the resulting flue-gas drive. This study presents a practical workflow to develop a phase behavior model in terms of SARA fractions (saturates, aromatics, resins and asphaltenes), which is aligned with the reaction modelling approach used in most kinetic models. The methodology requires conventional oil characterization (i.e. based on distillation cuts) and conventional phase behavior experiments (e.g. differential liberation), as well as oil characterization in terms of SARA fractions. The first step of the method consists of splitting of the heaviest oil fraction (i.e. plus fraction), followed by the lumping of all single-carbon-number components, in such a way that the new oil characterization honours the SARA data available, such as composition, and physical properties of each fraction (e.g. molecular weight). In addition, the gas components (e.g. Methane) would be treated as additional components as necessary. The second step is to tune an equation of state (EoS), in terms of the SARA-based model, to match the relevant laboratory experiments. Finally, the tuned EoS would be used to export the equilibrium constants (K-value tables) to the thermal numerical simulator. Different examples on the application of the phase behavior modelling workflow are presented and discussed in detail, for heavy and light oils. This work opens up opportunities to model the ISC process for any oil (i.e. light or heavy) by utilizing the currently available kinetic models, which in turn is an important step towards improving the predictability of ISC processes using reservoir simulation.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».