4.7 Article

Flow regime identification in the subsea jumper based on electrical capacitance tomography and convolution neural network

Journal

OCEAN ENGINEERING
Volume 266, Issue -, Pages -

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.oceaneng.2022.113152

Keywords

Electrical capacitance tomography; Flow pattern recognition; Jumper; Gas-liquid flow; Convolutional neural network

Funding

  1. Liaoning Provincial Natural Science Foundation of China [2020-HYLH-35]
  2. LiaoNing Revitalization Talents Program [XLYC2007092]
  3. 111 Project [B18009]
  4. Fundamental Research Funds of National Center for International Research of Subsea Engineering and Equipment [3132022354]
  5. Fundamental Research Funds for the Central Universities [3132022350, 3132022348]

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In this paper, a two-phase flow experimental system is constructed to identify the flow pattern of subsea jumpers using the EfficientNet-B5 convolutional neural network (CNN). The research achieves high accuracy in flow pattern recognition and provides a new method for monitoring flow patterns in deep-sea oil exploration and transportation.
The subsea jumper plays an indispensable role in the subsea production system as a connecting component between Christmas trees, manifolds and risers. Flow pattern changes inside the jumpers directly affect their safe operation. However, the research work on flow pattern identification of jumpers is limited at present. In this paper, the M-shaped jumper is chosen as the research object and two-phase flow experiment system is con-structed. In the range of gas superficial velocity 0-5 m/s and liquid superficial velocity 0.1-4 m/s, firstly, four typical flow patterns, namely, bubbly flow, slug flow, annular flow, and stratified flow, are collected by the electrical capacitance tomography (ECT) across the jumper to establish the flow pattern data sample set. Sec-ondly, the EfficientNet convolutional neural network (CNN) is used to train and learn the flow pattern. It is proved that EfficientNet-B5 is the most suitable and the flow pattern recognition accuracy reaches 95.6%. Finally, to enhance the recognition accuracy of similar flow patterns, EfficientNet-B5 is optimized by using the advanced optimizer Adam, with an accuracy rate of 97.55%. This research provides a new method for monitoring the flow pattern of deep-sea oil exploration and transportation, which is meaningful for the safe operation of the subsea jumper.

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