Optimizing oil and gas production forecasting using the subtraction-average-based optimizer

Abstract The application of machine learning algorithms in forecasting oil and gas production rates is critical for optimizing resource extraction and improving operational efficiency. Accurate predictions of future production rates enable operators to maximize yields, minimize downtime, and optimize resource management. This study proposes a new evolutionary Subtraction-Average-Based Optimizer (SABO), which updates searchers’ positions in the search space using the subtraction average of searcher agents. The SABO algorithm is integrated into each machine learning model such as Decision Trees, Random Forest, Extra Trees, Adaptive Boosting, Bagging, and Categorical Boosting, for performance improvement, and the resultant hybrid models are compared and examined using the test cases of the benchmark functions and the real-world dataset. The best performance belonged to the highest R 2 of 0.9327 and lowest RMSE of 2.0785 and MAE of 0.4665 for SABO-Bagging in the training set, and the test dataset recorded the best performance by the R 2 of 0.9267 and lowest RMSE of 2.2157 and MAE of 0.4558. These findings indicate that the SABO-Bagging hybrid model is the best performing model for the prediction of oil and gas production rates, and it has a competitive advantage compared to other machine learning methods for operational optimization in the energy industry.