Abstract
Enhanced oil recovery techniques like polymer flooding are vital for maximizing hydrocarbon extraction, but predicting recovery factors remains challenging due to complex reservoir dynamics. Traditional artificial neural networks offer predictive power but lack interpretability, limiting their practical utility in engineering applications. This study introduces Kolmogorov-Arnold Networks (KANs) as an interpretable alternative for modeling recovery factors in polymer flooding. Using a synthetically generated dataset of over 160,000 samples with key reservoir parameters, we trained KAN models through a series of experiments, varying network architectures, grid sizes, learning rates, and optimizers (Adam and LBFGS) to evaluate performance and interpretability. The best-performing KAN achieved a Test Mean Squared Error (MSE) of 0.000597 and a Test coefficient of determination$(\boldsymbol{R}^{2})$of 0.902 with only 1,885 parameters, closely matching the performance of a previous deep neural network model that used 43,265 parameters (Test$\mathbf{R}^{\mathbf{2}}$of 0.908). While KANs did not surpass the DNN in predictive accuracy, they offered comparable results with significantly reduced complexity and enhanced transparency. The modular structure and learnable activation functions of KANs provide insights into the decision-making process. This work contributes to the EOR field by demonstrating that KANs can effectively model complex reservoir behaviors while being transparent in their decision making.