Monitoring and Predicting CO2 Flooding Using Material Balance Equations
Notice bibliographique
Résumé
Abstract In order to operate a CO2 flooding scheme successfully, it is necessary to get accurate information about the reservoir dynamic performance and the fluids injected. Although 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 using different perspectives to provide valuable and complementary insights into the reservoir performance during the CO2 flooding process. The objective of this study is to develop models using material balance equations (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 the CO2 flooding process. 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 into 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 a free CO2 gas cap, the proportion of reservoir fluids contacted by CO2, the oil swelling and the oil relative permeability improvement. The model has been applied to analyze the Weyburn CO2 flooding project. The study has shown that the proposed MBE model is an effective complementary tool to analyze overall reservoir performance in tertiary CO2 recovery processes. The results show that:there exists a free CO2 gas cap under reservoir conditions, even if the reservoir pressure is larger than MMP (minimum miscible pressure) in the Weyburn Field;the CO2 partition ratios in oil, water and gas phases and the proportions of reservoir fluids contacted by CO2 largely affect the drive mechanism and production performance; andthe effect of CO2 solubility in water under actual reservoir conditions cannot be neglected. The proposed new model is the first one in developing and applying MBE to evaluate the overall dynamic performance for the CO2 flooding process and a valuable insight into reservoir responses during this process has been achieved. Introduction CO2 flooding is considered one of the most effective tertiary recovery processes in light/medium oil reservoirs and has achieved widespread use in the petroleum industry. However, the complicated displacement mechanisms and reservoir performance involved in the CO2 injection process have not been completely understood. Monitoring reservoir performance and obtaining accurate information regarding reservoir fluid and injected fluid using field data will help understand the mechanisms and manage the CO2 injection project efficiently. There are two types of methods that monitor and evaluate reservoir performance: numerical simulation and MBE. MBE is a classic reservoir engineering tool. It is applied to analyze the reservoir performance based on the law of conservation of matter. Compared with MBE, reservoir numerical simulation is a more modern technique for modelling reservoir performance.
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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) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| 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 ».