Notice bibliographique
Résumé
Abstract Some solution-gas-drive heavy oil reservoirs (foamy oil reservoirs) in Canada, Venezuela, China and Oman have demonstrated unusually high primary oil recovery factor (>10%), low production gas-oil ratio, low reservoir pressure decline, and high oil production rate. One of the hypotheses to explain these characteristics is that bubbles released as a result of pressure depletion, break up into smaller bubbles. Small bubbles have lower tendency to move. In foamy oil reservoirs, the driving force to produce oil is the force exerted by the bubbles expansions. Therefore, if a mechanism tends to keep the bubbles in the reservoir, more oil can be produced. In this study a pore-scale model of porous media was developed to investigate the effect of different parameters such as pressure gradient, oil viscosity, interfacial tension, contact angles or wettability, and pore aspect ratio on the break-up of a solitary bubble. An important result is that, bubble break-up is more likely to happen in network model runs with high pressure gradients. Also the model shows that bubble break-up time increases with increasing oil viscosity. Therefore, it is unlikely that the large number of small bubbles in foamy oil flow created by bubble break-up. Other mechanisms such as hindered bubble coalescence, and ramp out bubble nucleation might be the important mechanisms. Introduction Some solution-gas-drive heavy oil reservoirs in Canada, Venezuela, China and Oman have experienced unusually high primary production rates, high primary oil recovery factors (>10%), low producing gas oil ratio, and low reservoir pressure decline(1). To explain the unusual behaviour, three hypotheses have been advanced: geomechanical effects(2); special fluid properties(3) and nusual flow dependent properties of oil and gas(4). For the last category, it is believed that the low mobility of gas accounts for the unusual behaviour in solution-gasdrive heavy oil reservoirs. One of the mechanisms that contribute in keeping the gas dispersed in foamy oil and its mobility low could be gas bubble break-up. An interplay of bubble nucleation, expansion, coalescence and break-up determines the micro-structure and dynamics of internal gas-oil dispersion. Knowledge of these pore level events is necessary to derive physically meaningful rate expressions for these processes for further implementation in a macroscopic scale fluid flow model such as reservoir simulators or in mechanistic models. As yet, a reservoir simulator that includes the above mechanisms has not been developed. We have used a network model of porous media to investigate the mechanism of bubble break-up at pore level. Network models are simplified mathematical representation of the 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, so that related fluid flow and interface movement can be treated mathematically at a manageable level of complexity. In this work, a previously developed micro-flow simulator(5) (that includes viscous and capillary forces) was modified to include the bubble rupture mechanism (break-up). The intent is to be able to predict conditions for bubble break-up at system parameters such as viscosity and pressure gradient.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
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 | 0,000 | 0,001 |
| 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) | 0,001 | 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 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 ».