Identification of two-phase flow patterns in Z-shaped offshore pipelines based on deep learning technologies

Abstract

Flow pattern identification is of great significance for the flow assurance of multiphase flow in offshore pipelines. For this purpose, a deep learning algorithm integrating convolutional neural networks (CNNs) and long short-term memory (LSTM) networks is developed in this study to identify different flow patterns of two-phase flow in a Z-shaped pipeline. To verify the applicability and performance of the proposed algorithm, a series of experiments are conducted in the multiphase flow loop. Then, the flow pattern pictures, mass flow rates, and vibration signals are collected by the NI data acquisition system and subjected to denoising using the density-based spatial clustering of applications with noise (DBSCAN) algorithm combined with LSTM. Meanwhile, the Kendall rank correlation is employed for feature processing. Finally, the proposed deep learning algorithm achieves accuracies of 0.98077 with engineering parameters and 0.955 with time-domain parameters. In addition, a comparison analysis is conducted with four other common deep learning algorithms in terms of computation time, learning rate, accuracy, and different sample quantities. The results demonstrate that the proposed algorithm can not only improve the identification accuracy but can also achieve faster convergence speed and higher computational efficiency.