a hybrid artificial neural network framework for high-fidelity estimations of well performance

This study develops a hybrid ml model that integrates an mlp neural network with a ga for reliable well performance forecasting. the model utilizes four readily available wellhead parameters: choke diameter, wellhead pressure, gas-liquid ratio, and basic sediment and water content, with the liquid production rate as the target output. a rigorous data preprocessing step, including outlier removal, was applied to enhance dataset quality. the optimized mlp-ga model demonstrated high predictive accuracy, achieving r2 values of 0.9983, 0.9989, and 0.9914 on the training, validation, and test sets, respectively, while maintaining a very low mse.