A Data Mining Approach to Assess Field Scale CO2 Enhanced Oil Recovery and Sequestration Performance Correlated to Geological and Reservoir Characteristics
Notice bibliographique
Résumé
Summary Carbon dioxide (CO2) injection has gained popularity in the petroleum industry as a dual-purpose method for enhanced oil recovery (EOR) and long-term carbon sequestration. However, assessing the performance of CO2 EOR and its storage potential across large-scale fields is a complex task, primarily due to the heterogeneous geological characteristics of reservoirs and the dynamic behavior of injected CO2. Traditional methods for evaluating CO2 injection often rely on manual interpretations or computationally expensive reservoir simulations, both of which can be biased, time-intensive, and less effective for fieldwide analyses involving extensive data sets. In this study, a data mining-driven methodology was developed and applied to one of the most prominent CO2 injection projects in the world. More than 2,000 wells with decades-long production histories were analyzed using advanced statistical and geostatistical approaches, including spatial and temporal normalization of production data. By correlating key production metrics with geological features inferred from the data, fracture-dominated and matrix-dominated regions within the field were identified. The analysis further highlighted zones with differing CO2 injection efficiency and oil displacement behavior, providing a comprehensive understanding of reservoir performance in terms of oil recovery and CO2 sequestration. A critical aspect of the methodology involved combining multiple production metrics—such as gas/oil ratio (GOR), water cut (WCT), time to peak production, and CO2 breakthrough patterns—using Z-score-based normalization across both spatial and temporal domains. This approach enabled localized trend interpretation while maintaining consistency with physical reservoir behavior. Zones where CO2 injection was successful in both enhancing oil recovery and sequestering carbon were differentiated from areas where CO2 rapidly broke through without effective oil displacement, primarily due to fracture orientations and density (less vertically oriented fractures or matrix system dominated reservoir sections). Additionally, regions dominated by vertical fractures, which contributed to long-term CO2 storage, were identified. The results of this work provide valuable insights for optimizing CO2 injection strategies and improving sweep efficiency, ultimately aiding in better decision-making for both enhanced recovery and greenhouse gas sequestration. This novel approach bridges the gap between data-driven analysis and traditional reservoir engineering principles, offering a scalable framework for CO2 EOR operations in fields with complex geologies.
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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 ».