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

Abstract

Background and Purpose: 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 substantial domain expertise. Machine learning (ML) offers a data-driven approach for predicting production trends, optimizing operational strategies, and supporting reservoir-management decisions. This study evaluates multiple ML approaches, including regression, decision trees, random forests, gradient boosting machines (GBM), and deep learning, for oil well production forecasting and reservoir optimization.

Methods: A synthetic dataset representing reservoir conditions, production histories, and operational parameters was used to train and evaluate ML models. Linear regression, decision trees, random forests, GBM, and deep-learning models were assessed using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Hyperparameter tuning and cross-validation were applied to improve predictive performance, while feature-importance analysis was used to identify factors influencing production.

Findings: GBM achieved the highest reported forecasting accuracy, with an RMSE of 3.5% and an MAE of 2.1%. Deep-learning models captured complex production patterns but required greater computational resources. Random forests demonstrated strong generalization on noisy data, whereas linear regression was less effective for non-linear reservoir behavior. Overall, the evaluated ML approaches improved forecasting capability and supported real-time optimization of reservoir operations.

Theoretical Contributions: The study demonstrates how data-driven learning methods can complement conventional reservoir-engineering approaches by modeling non-linear production behavior, supporting comparative evaluation of forecasting algorithms, and identifying operational variables that influence reservoir performance. The simulation-based framework provides a structured basis for examining ML-assisted reservoir management under controlled conditions.

Conclusions and Policy Implications: Machine learning can enhance oil well production forecasting and reservoir management by improving predictive accuracy, supporting operational optimization, and strengthening data-driven decision-making. GBM showed the strongest balance between accuracy and efficiency in the reported analysis. Future implementation should emphasize validation with real-world reservoir datasets, model interpretability, robust data governance, and hybrid approaches that combine ML with physics-based reservoir simulations.