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Flow Pattern and Resistance Characteristics of Gas–Liquid Two-Phase Flow with Foam under Low Gas–Liquid Flow Rate

Author

Listed:
  • Bin Wang

    (Changqing Engineering Design Co., Ltd., Xi’an 710020, China)

  • Jianguo Hu

    (Changqing Engineering Design Co., Ltd., Xi’an 710020, China)

  • Weixiong Chen

    (State Key Laboratory of Multiphase Flow in Power Engineering, Xi’an Jiaotong University, Xi’an 710049, China)

  • Zhongzhao Cheng

    (Changqing Engineering Design Co., Ltd., Xi’an 710020, China)

  • Fei Gao

    (Changqing Engineering Design Co., Ltd., Xi’an 710020, China)

Abstract

To reduce the cost of arranging air foam flooding equipment at each wellhead, a method of establishing centralized air foam flooding injection stations is proposed. The flow pattern and resistance characteristics of air foam flooding mixtures in different initial conditions are studied. Experimental results indicate that the probability density function of stratified flow is obtained by comparing stainless steel and transparent pipes. If the gas–liquid ratio is kept constant, then the shape of the probability density function remains unchanged in both stainless steel and transparent tubes. Meanwhile, the flow pattern under the gas–liquid ratio is determined by comparing the image recognition results with the probability density function, and a formula for calculating the resistance and pressure drop of the gas and liquid two-phase flow in the horizontal and upward pipes is established. Compared with the experiments, the error results of the calculation are small. Thus, the proposed equations can be used to predict the flow resistance of real air foam flooding.

Suggested Citation

  • Bin Wang & Jianguo Hu & Weixiong Chen & Zhongzhao Cheng & Fei Gao, 2021. "Flow Pattern and Resistance Characteristics of Gas–Liquid Two-Phase Flow with Foam under Low Gas–Liquid Flow Rate," Energies, MDPI, vol. 14(13), pages 1-15, June.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:13:p:3722-:d:579322
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    Cited by:

    1. Xinping Li & Nailiang Li & Xiang Lei & Ruotong Liu & Qiwei Fang & Bin Chen, 2023. "Study on Artificial Neural Network for Predicting Gas-Liquid Two-Phase Pressure Drop in Pipeline-Riser System," Energies, MDPI, vol. 16(4), pages 1-13, February.

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