A Theoretical Model for Dynamic Performance Prediction of Air-Foam Flooding in Heterogeneous Reservoirs
Notice bibliographique
Résumé
Abstract Air-foam flooding has already been pilot-tested and approved as a feasible and promising EOR method in tight oil reservoir. This study is to develop a simple but effective model to predict dynamic performance of air-foam flooding by considering main physical and chemical mechanisms in this process, such as gas channeling caused by mobility difference, flue gas driving and instability of foam. According to the instability of foam, a new model is proposed to estimate recovery factor, which separates foam flooding status into three areas, including gas, water and foam area. Prediction of breakthrough time is critical for this model, which is estimated according to relation between area being swept and area to be swept by cycle of injected slug. Once the breakthrough time of gas and surfactant solution are estimated, dynamic performance of every stage during air-foam flooding is predictable. Relation between recovery factor and production time or PV can be predicted, if essential reservoir, fluid and operational parameters are provided. Relative numerical simulation studies on homogeneous case and heterogeneous case are both introduced to validate proposed model. Results of comparison suggest this model is highly consistent with the numerical simulation results. The most extreme difference in recovery factor after ten years between proposed method and simulation is less than 6.2%, which is less than 2.5% in most case. Meanwhile, this model requires much less input data than numerical simulation for dynamic performance prediction, which makes it a really convenient tool to evaluate potential of an air-foam flooding project. Sensitivity analysis is introduced to study effects of variation in parameters on the performance of air-foam flooding project, including fluid injection rate, slug size, slug proportion and reservoir heterogeneity. The higher liquid ratio is injected in each slug, the better recovery factor is obtained. However, there isn't much difference once liquid ratio is higher than 50%. Recovery factor increases with higher fluid injection rate. Meanwhile, the increasing rate of recovery factor drops as fluid injection rate increases. Also, optimum slug size exists which means any slug size being higher or lower this value would result in lower recovery factor. These conclusions can help get optimized operational parameters once economic data are settled. This proposed model considered major mechanisms in air-foam flooding and related reservoir, fluid and operational parameters, and provided a fast approach to predict dynamic performance of air-foam flooding and can be used as a tool to optimize the operational parameters. The core idea of this method, such as the estimation of breakthrough time, also provides a new approach to estimate the performance of other immiscible flooding method.
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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 ».