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Du, M., Yin, H.G., Chen, X.Y. and Wang, X.Q. (2019) Oil-In-Water Two-Phase Flow Pattern Identification from Experimental Snapshots Using Convolutional Neural Network. IEEE Access, 7, 6219-6225.
https://doi.org/10.1109/ACCESS.2018.2888733

has been cited by the following article:

• TITLE: Classification of Oil-Gas-Water Three-Phase Flow in a Pipeline Based on BP Neural Network Analysis

  AUTHORS: Wenjing Lu, Peng Li, Xuhui Zhang

  KEYWORDS: BP Neural Network, Flow Pattern, Two-Phase Flow, Dimensionless Controlling Parameters

  JOURNAL NAME: Journal of Data Analysis and Information Processing, Vol.10 No.4, September 21, 2022

  ABSTRACT: The flow pattern in a pipeline is a very important topic in petroleum exploitation. This paper is to classify the flow pattern of oil-gas-water flow in a pipeline by using BP neural network. The effects of different parameter combinations are investigated to find the most important ones. It is shown that BP neural network can be used in the analysis of the flow pattern of three-phase flow in pipelines. In most cases, the mean square error is large for the horizontal pipes. The optimized neuron number of the middle layer changes with conditions. So, we must changes the neuron number of the middle layer in simulation for any conditions to seek the best results. These conclusions can be taken as references for further study of the flow pattern of oil-gas-water in a pipeline.