A Novel Fusion: Integrating Artificial Neural Networks and Production Fit Models for Swift and Coherent Data-Physics Predictive Modeling of a Mature Oil Field in Texas

This study presents an innovative approach that combines Artificial Neural Networks (ANNs) with traditional production fit models to improve predictive modeling in a mature Texas oil field. By analyzing data from 398 wells, the model achieves a high Spearman correlation factor of 0.71, indicating strong predictive accuracy. This hybrid approach enhances reservoir management by leveraging both data-driven insights and established empirical methods, ultimately aiding in more effective decision-making and optimization strategies.

Traditional production models rely on deterministic and empirical methodologies, which are grounded in physical laws and historical data. While these models provide a robust framework for understanding reservoir behavior, they struggle with real-time data integration and adaptability. Machine learning approaches, on the other hand, process vast datasets efficiently and identify complex patterns that may not be immediately apparent through conventional analysis. However, these models often lack physical constraints, leading to predictions that may be unrealistic or impractical, especially when applied to scenarios beyond their training data.

By integrating both methodologies, this study ensures a balance between predictive accuracy and physical realism. Traditional models contribute a structured foundation, ensuring that predictions align with established engineering principles, while machine learning enhances adaptability and pattern recognition. This fusion allows for a more reliable and flexible predictive model that improves decision-making in complex reservoir conditions. The results demonstrate that this hybrid approach is particularly valuable in oil and gas operations, where quick and accurate predictions are essential for optimizing production strategies.