A Comprehensive Workflow for Near Real Time Waterflood Management and Production Optimization Using Reduced-Physics and Data-Driven Technologies
Notice bibliographique
Résumé
Abstract Optimizing waterfloods in large fields with complex geology can be an extremely difficult engineering problem. Simulations become overwhelming and tremendously time consuming, while basic classical engineering will not capture enough physics to assess well by well decisions. In this paper we present a novel data-driven, reduced-physics workflow to manage and optimize an exceptionally complex reservoir in Latin America. The first stage of the workflow involves collecting and validating the field data, including rock and fluid properties, production, injection and pressure data as well as well information, such as trajectories and historical perforations. The reservoir behavior is then modeled following an approach similar to the one by Thiele and Batycky (2006) in the context of streamline simulation. The model represents the reservoir as a network of inter-well connections described by their strength and efficiency. The strength of connection is determined through the solution of a numerical tracer test rather than through streamline-based method like in Thiele and Batycky (2006), which generalizes the method to unstructured or locally refined grids as well as dual permeability systems and allows the method to account for compressibility effects. An empirical fractional flow model is then used to calculate the connection efficiencies. Once the model is complete and calibrated, a cutting-edge optimization algorithm is used to optimize the production-injection strategy based on this network of subsurface connections. Recommendations for adjustments in the production-injection strategies are proposed and model uncertainties are computed through a novel algorithm to compute the associated risks. The proposed methodology was successfully applied to a giant Latin American waterflooded reservoir with over 800 wells and nearly a hundred years of history. The approach identified an optimized strategy that could deliver a 5% increase in oil production with a 10% reduction in water production. The methodology proposed is fast enough to build and match a new model in a few days; and updating an existing model takes less than an hour as new data comes available. The proposed method was designed to avoid expensive numerical simulation and to simplify the history-matching process. It can therefore be used daily to help engineers optimize the production-injection strategy of reservoirs. Furthermore, the model can be used for robust short-term forecasting as well as relatively elaborate production mapping.
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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 ».