Kolmogorov–Arnold Network for Predicting CO2 Corrosion and Performance Comparison with Traditional Data-Driven Approaches

Abstract

Accurate prediction of CO2 corrosion under dense-phase and supercritical conditions remains a critical challenge for oil and gas pipeline integrity management. While machine learning (ML) has been applied in this field, prevailing models like the Multilayer Perceptron (MLP) often struggle to capture the complex, non-linear interactions between multiple environmental parameters, limiting their predictive accuracy and robustness. To bridge this gap, this study innovatively introduces the Kolmogorov–Arnold Network (KAN) algorithm for CO2 corrosion rate prediction. Utilizing a unique dataset of field-collected parameters (including dissolved O2, H2S, SO2 concentrations, and water cut), we developed a KAN model and conducted systematic hyperparameter optimization. Our investigation revealed the optimal network configuration (3 layers, grid = 3) and, counterintuitively, that the steps parameter does not correlate positively with performance. Most significantly, comparative experiments demonstrated that the KAN model substantially outperforms traditional MLP, achieving superior prediction accuracy alongside faster computational speed and lower loss values. These findings not only provide a robust tool for precise corrosion prevention in oilfield operations but also highlight the potential of KAN as a novel, efficient, and highly accurate framework for tackling complex problems in materials degradation.