Assessment of Oil Recovery Methods for Reservoirs in the Flemish Pass Basin
Notice bibliographique
Résumé
Summary In recent years, the Flemish Pass Basin has been gaining momentum as an area of potential high-volume resources on the frontier of remote, deep-water offshore oil development. This simulation study utilizes three sector models representing regional, discovered reservoirs, and two tuned fluid models representing oil sampled from wells in the Flemish Pass Basin. Generally speaking, WAG is considered a late-life enhanced oil recovery (EOR) technique, while implementing WAG immediately upon first oil for secondary recovery is less common; however, may be equally or more valuable. This study aims to evaluate three secondary oil recovery methods, water flooding, gas flooding, and water-alternating-gas (WAG) flooding. Each recovery method is simulated with Schlumberger’s ECLIPSE reservoir simulator and uses a combination of three distinct reservoir geo-models and two fluid models. This study is a sensitivity analysis using geo-models that represent three discovered regions and two sampled fluids from the Flemish Pass Basin. The study is aimed at evaluating the effects of the various recovery methods over a duration of either five- or twenty-year forecast periods. Results from this study capture an inherent uncertainty by drawing from eighteen simulation cases to quantify the relative benefit of each recovery method. These results indicate that using WAG as a secondary recovery method can yield a 4% to 10% increase in recovery over water or gas flood, and that secondary WAG can extend a well pair’s production plateau by up to 80% in specific circumstances. Further observations indicate that secondary WAG in light oil reservoirs yield a ∼10% increase in recovery over secondary water or gas flooding. Using WAG in a medium oil reservoir yields a 4% to 9% increase in recovery over water flood, and a 2% to 16% increase in recovery over gas flood. In terms of geology, WAG is observed to be most valuable in ultra-high-quality reservoirs. The better the reservoir quality, the more recovery improvement. In terms of fluids, the medium oil responds best to the gas injection phase of WAG while the light oil appears to respond well to both phases. During development optimization, these trends can be accounted for in the injection cycle timing and duration for each phase. In terms of using WAG as a tertiary recovery method after a period of water or gas flooding, tertiary WAG is observed to be most beneficial in the low to medium quality reservoirs. Tertiary WAG extends the production duration and results in a ∼4% increase in recovery beyond water flooding. Study results go on to quantify the differences in water and gas breakthrough as a factor of pore volume injected (PVI) and conclusions further indicate which reservoirs are best suited for each recovery method.
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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 | 0,004 | 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 ».