Abstract
Polymer flooding is a cost-effective and successful method for enhancing oil recovery and is the focus of this study which utilized the Buckley-Leverett mathematical model to create over 163,000 synthetic samples representing various reservoir conditions using seven input parameters. To mimic real-world conditions and reduce overfitting, artificial noise was added to the dataset. Two neural networks were trained with this data, both achieving an R² (coefficient of determination) greater than 0.906, indicating high predictive accuracy. The study also conducted permutation feature importance analysis to identify crucial parameters in both models. This research, with its extensive and realistically simulated dataset and sophisticated machine learning techniques, provides a robust framework that can enhance decision- making in oil production by pinpointing key parameters and forecasting the oil recovery factor based on specific input values.