An Integrated Fracture Characterization of a Heavy Oil Naturally Fractured Carbonate Reservoir
Notice bibliographique
Résumé
Abstract This paper presents an integrated approach to fracture characterization in a massive, naturally fractured carbonate, heavy oil reservoir. Such information is critical in understanding the reservoir drive mechanism and predicting recovery from this field. The characterization combines various techniques from geophysics, geology, petrophysics and engineering, including core data, Formation Micro Scanning (FMS) data, analogous field data, pressure buildup tests and inflow performance results. The description of the fracture system includes the number of fractures and their distribution, spacing, orientation, aperture and porosity. This information provides the basis for building a full field, three-dimensional reservoir model, which is then calibrated with production performance data. Introduction Developing a massive, naturally fractured carbonate, heavy oil reservoir in folded thrust belts presents extreme reservoir engineering and geological challenges and opportunities. These types of reservoirs are the extreme setting for large-scale high contrast and discontinuous reservoir properties1. Horizontal wells in such a reservoir with bottom water drive allow economic development because of lower production drawdown pressures and reduced water coning. Yet, targeting optimum horizontal well location, length, spacing and recovery prediction represent a great challenge because it is sometimes unclear where flow is coming from. To accomplish optimal horizontal well placement, a fracture model needs to be developed first because the fracture spacing, geometry and permeability affect reservoir drive mechanisms as well as fracture-matrix cross flow. Fracture spacing, porosity and aperture as well as fracture distribution will be critical factors in determining the production and recovery factor for water drive in naturally fractured reservoirs. This paper ties the geological and engineering models together to determine a representative set of fracture parameters. It discusses the integrated forward (geological) and inverse (engineering) techniques used to improve the characterization of fractures. The results were used in reservoir model simulation and subsequent application in reservoir management of the Cuban Puerto Escondido heavy oil field. CUBAN PUERTO ESCONDIDO FIELD The geology of the Cuban Puerto Escondido North Coast oilfield is complex both stratigraphically and structurally (Figure 1). The hydrocarbon trap exists as a structural stack of thrust sheets in the Jurassic/Cretaceous carbonates of the Cifuentes and Ronda formations of the Veloz Group. Various porosity types are present; however, the dominant factor in oil migration and production is fracturing. The fields have multiple stacked thrust sheets. Each of these thrust sheets was faulted up and over undisturbed sections from south to north. Each subsequent thrust carries with it "piggy-back" all the preceding thrusts, creating an ever-higher stack of thrust sheets. The Cuban North Coast Veloz fields are also typically bounded on the east and west by SW to NE trending strike slip faults that probably were activated at the time of thrust faulting and occasionally continued to be active until recent time. To date, twelve wells have been drilled in this field including ten horizontal wells to produce oil from several thrust sheets. Many wells have produced the 9 - 12 °API oil at rates of 300 to 500 m3/d.
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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 | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 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 ».