Application of Machine Learning for Optimizing Oil Well Production and Reservoir Management: A Simulation-Based Approach

Background: The oil and gas industry requires efficient reservoir management and accurate production forecasting to optimize operations and reduce costs. Traditional physics-based models, though reliable, are computationally intensive and require domain expertise. Machine learning (ML) offers a data-driven approach to predict production trends, optimize operational strategies, and enhance decision-making. This study evaluates various ML models, including regression, decision trees, gradient boosting machines (GBM), and deep learning, to determine their effectiveness in oil well production forecasting. Methods: A synthetic dataset simulating reservoir conditions, production histories, and operational parameters was used to train ML models. Linear regression, decision trees, random forests, GBM, and deep learning models were tested. Performance was measured using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Hyperparameter tuning and cross-validation were applied to improve model accuracy, and feature importance analysis was conducted to identify key factors influencing production. Results: GBM achieved the highest accuracy, with an RMSE of 3.5% and an MAE of 2.1%, outperforming other models in production forecasting. Deep learning models captured complex patterns but required high computational resources. Random forests showed strong generalization, making them effective for noisy datasets, while linear regression struggled with non-linearity. Overall, ML models improved forecasting accuracy and enabled real-time optimization of reservoir operations. Conclusion: ML models significantly enhance oil well production forecasting and reservoir management. GBM proved to be the most effective, balancing accuracy and efficiency. Integrating ML into oil well operations can reduce costs and improve decision-making. Future research should focus on real-world datasets and hybrid ML approaches to further refine predictive capabilities.