Oil-in-water two-phase flow pattern identification from experimental snapshots using convolutional neural network

Abstract

The flow pattern is one of the most significant parameters in modeling the oil–water two-phase system. How to extract efficient and objective features to precisely identify the oil–water two-phase flow patterns is still a significant issue. Inspired by the deep learning hierarchically feature extraction way, we, in this paper, employ convolutional neural networks (CNNs) to identify oil–water two-phase flow patterns. First, we carry out oil–water two-phase flow experiment and collect different oil–water flow pattern images. Then, we propose an image segment algorithm based on the minimum gray level to obtain the interest region that reflects the flow pattern characteristics. Finally, we employ three frequently used CNNs, LeNet-5, AlexNet, and VGG-16 net, to extract the image features and identify typical oil–water two-phase flow patterns. The results show that networks with more deep structures preserve relatively high flow pattern recognition accuracy. This paper provides a novel application of the deep learning method for the oil–water two-phase flow identification.