Micro-Model Experiments and Network Modelling of Bubble Growth in Foamy Oil Flow
Notice bibliographique
Résumé
Abstract The subject of bubble nucleation, growth, and movement in some of the solution-gas-drive heavy oil reservoirs (foamy oil) has been examined in the last few years. The behaviour of foamy oil reservoirs is different from conventional oil reservoirs in terms of pressure decline, oil recovery, and production gas-oil ratio. We studied the mechanism of bubble growth in an etched glass micro-model of porous media with concomitant network modelling. Bubble growth from sizes smaller than a pore to the sizes covering several pores is investigated. Movements of the gas-liquid interfaces at different stages of growth are examined. The force balances on bubbles at different stages of expansion is studied for two cases where the size of a bubble is smaller than the pore body and where bubble has grown enough to make contact with pore walls and invade the adjacent pore throats. Finally, a network model of porous media (pore level simulator), which includes viscous, capillary and pressure forces is developed to follow bubble growth as a result of pressure depletion in a porous medium. Effects of oil viscosity, pressure depletion rate, wettability, and capillarity on the size of a solitary bubble are investigated. Increased oil viscosity hinders bubble growth and bubbles become unstable and break-up after limited expansion. Introduction Some solution-gas-drive heavy oil reservoirs in Canada, Venezuela, China and Oman have demonstrated unusually high primary production rates, high primary oil recovery factors (>10%), low producing gas-oil ratio, and low reservoir-pressure-decline(1,2). To explain these anomalies, three hypotheses have been advanced: geomechanical effects (3), special fluid properties (4), and unusual flow dependent properties of the oil and gas (5). For the last category, it is believed that local pore-level processes involving the bubble formation, growth, breakup, and coalescence account for the unusual behaviour in solution-gas- drive heavy oil reservoirs. An interplay of bubble formation, growth, coalescence and break-up determines the critical gas saturation and relative mobility of the gas phase. Knowledge of these pore level events is necessary to derive physically meaningful rate expressions for modeling fluid flow on a macroscopic scale such as reservoir simulators or in mechanistic models. Rock properties such as porosity and permeability, fluid properties such as viscosity and composition, and reservoir rock/fluid characterization such as spreading coefficient and wettability affect the local events for bubbles. The topology and morphology of a porous medium affect the mechanism of bubble growth. Therefore, a bubble would expand differently in a porous structure compare to in an open system. Dominguez et al. (6) have demonstrated that the shape and size of bubbles in a network of porous medium and in a Hele-shaw cell are very different. This study is related to describing foamy oil flows when bubbles grow at pore-size level. Network models are simplified mathematical representation of real porous material. The objective of a network model is to provide a reasonable idealization of the complex geometry of the real porous medium at microscopic scale or pore-level, so that related fluid flow and interface movement can be treated mathematically at a manageable level of complexity.
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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 ».