Machine learning technique for the identification of two-phase (oil-water) flow patterns through pipelines

Abstract

This study develops a robust machine learning model based on artificial neural networks to classify six flow patterns in oil-water two-phase flow within horizontal pipelines, a key aspect for ensuring operational efficiency, integrity, and cost-effective design in the oil and gas industry. A database comprising 1,846 experimental data points was assembled from the literature, encompassing various operating conditions, including fluid properties, superficial velocities, and pipe diameters. After evaluating 104 network configurations, the optimal model was selected, achieving an overall accuracy of 95.4%, with training, validation, and testing accuracies of 97.1%, 92.8%, and 90.3%, respectively, and a cross-entropy error of 0.024. The model demonstrated rapid convergence with a training time of only 2 s, making it a reliable and computationally efficient tool for flow pattern recognition. The outcomes of this study provide significant value for improving pipeline design, optimizing flow assurance strategies, enhancing corrosion control, and supporting real-time operational decision-making in multiphase transport systems in the oil and gas industry.