Optimizing oil production forecasts in Iranian oil fields: a comprehensive analysis using ensemble learning techniques

This study introduces the application of Stacking Ensemble Learning in petroleum engineering, marking a significant advancement in oil production rate forecasting. Unlike traditional forecasting methods, which often rely on single-model approaches with limited adaptability to complex, the methodology integrates multiple machine learning algorithms each optimized using distinct, hyperparameter tuning techniques. The inclusion of advanced optimization strategies, such as Genetic Algorithm (GA), Teaching-Learning-Based Optimization (TLBO), and Particle Swarm Optimization (PSO), ensures that each base model reaches its highest potential performance. By employing a diverse and extensive dataset of 7245 data points from 10 oil wells, the study achieves a high level of model generalizability across varying operational conditions, including choke sizes, wellhead pressures, and gas-oil ratio values. The ensemble approach demonstrated superior performance compared to individual models, with significant improvements in prediction accuracy and robustness. Models such as Radial Basis Function Neural Network (RBFNN) (74.4%), DNN-TLBO (93.3%), and DNN-GA (92.8%) exhibited enhanced capabilities but were outperformed by the integrated ensemble method (93.9%), highlighting its effectiveness in oil production forecasting. This work presents a novel application of Stacking Ensemble Learning in petroleum engineering, offering an innovative solution for improving oil production rate forecasting. The study’s integration of diverse optimization techniques and ensemble modeling provides new insights and tools to advance predictive analytics in the oil and gas industry.