A Conceptual Model for Improving Reservoir Performance Predictions using Deep Learning and Temporal Data Analysis

Accurate reservoir performance prediction is essential for optimizing resource extraction, improving operational efficiency, and supporting sustainable reservoir management, yet traditional physics-based and empirical models struggle with complex temporal patterns and nonlinear dynamics. This paper proposes a conceptual model that integrates advanced deep learning techniques with temporal data analysis to address these limitations and enhance prediction accuracy. The model leverages historical data trends, real-time updates, and adaptive learning mechanisms to support anomaly detection, production optimization, and more informed decision-making. It emphasizes the integration of domain-specific reservoir engineering knowledge to improve interpretability and ensure compatibility with existing operational workflows. Practical applications include reducing operational costs, minimizing resource wastage, and advancing reservoir engineering practices. The study concludes with recommendations for refining the conceptual framework, exploring hybrid modeling approaches, and ensuring scalability across diverse and complex reservoir conditions, demonstrating the transformative potential of combining deep learning with temporal insights for modern reservoir management.