Monitoring and Predicting CO2 Flooding Using Material Balance Equation
Notice bibliographique
Résumé
Abstract Operating a CO2 flooding scheme successfully requires the capacity to get accurate information of the reservoir dynamic performance and the fluids injected. Though some numerical simulation studies have been conducted, the complicated drive mechanisms and actual reservoir performance have not been fully understood. Thus, there is a strong industrial need to develop models from different perspectives that may be obscured by current simulators to provide valuable and complementary insights into the reservoir performance during CO2 flooding process. The objective of this study is to develop models using material balance equation (MBE) to analyze the field data before and after CO2 injection. After matching the historical field data the proposed model can be applied to evaluate, monitor and predict the overall reservoir dynamic performance during CO2 flooding process. In order to accurately account for the complex displacement process involving compositional effect and multiphase flow, the PVT properties of reservoir fluids and the four-phase fluid relative permeability relationship are integrated in the model. This study has investigated the effects of a number of factors, such as the reservoir pressure, the amount of CO2 injected, the CO2 partition ratios in reservoir fluids, the possibility of the existence of free CO2 gas cap, the proportion of reservoir fluids contacted by CO2, the starting time of CO2 flooding, the oil swelling, and the oil relative permeability improvement when mixing with CO2, etc. This study has shown that the proposed MBE model is an effective complementary tool to analyze/monitor the overall reservoir performance in tertiary CO2 recovery process. The model has been applied to analyze the Weyburn CO2 flooding project as an example. MBE analysis also indicated that:there exists a free CO2 gas cap under reservoir condition even if the reservoir pressure is larger than MMP in the Weyburn oil field,the CO2 partition ratios in oil, water and gas phases and the proportions of reservoir fluids contacted by CO2 affect largely the drive mechanism and production performance, further experimental study is recommended,a higher oil recovery can be obtained when CO2 flood is initiated at an early life time of the reservoir, andthe effect of CO2 solubility in water under actual reservoir condition cannot be neglected. The proposed new model is the first one in developing and applying MBE to evaluate the overall dynamic performance for CO2 flooding process and a valuable insight into reservoir responses during this process has been attained. Introduction CO2 flooding is considered one of the most effective tertiary recovery processes in light/medium oil reservoirs and has achieved widespread use in petroleum industry. However, the complicated displacement mechanisms involved in the CO2 injection process have not been completely understood. Monitoring the reservoir performance and obtaining the accurate information regarding the reservoir fluid and injected fluid using actual field data will help to understand the mechanisms and manage the CO2 injection project efficiently. There are two types of methods that can monitor and evaluate reservoir performance: numerical simulation and MBE. MBE is a classic reservoir engineering tool.
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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) | 0,002 | 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 ».