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Fuzzy inference system application for oil-water flow patterns identification

Author

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  • Wu, Yuyan
  • Guo, Haimin
  • Song, Hongwei
  • Deng, Rui

Abstract

Prediction of oil-water two-phase flow pattern provides an effective solution for reducing oil production costs. In this research, the fuzzy inference system (FIS) is utilized to predict fluid flow patterns and establish a new adaptable prediction model. This paper takes No. 10 industrial white oil and tap water as the research objects to simulate fluids, and analyzes the changes of the pipeline angle, the total flow of oil-water two-phase flow and the convective pattern of water cut. A data set containing 60 samples was used to create the model, and the Mamdani fuzzy model was established using MATLAB software. The results show that compared with the BP neural network algorithm, the model set forth in the present paper has higher accuracy and reliability, and can achieve real-time monitoring and effectively reduce errors, especially in the case of decision-making. In addition, the fuzzy model is demonstrated that in the entire production logging process of non-vertical wells, the use of a fuzzy inference system to predict fluid flow patterns can greatly save production costs while ensuring the safe operation of production equipment.

Suggested Citation

  • Wu, Yuyan & Guo, Haimin & Song, Hongwei & Deng, Rui, 2022. "Fuzzy inference system application for oil-water flow patterns identification," Energy, Elsevier, vol. 239(PD).
    • Handle: RePEc:eee:energy:v:239:y:2022:i:pd:s0360544221026086
    DOI: 10.1016/j.energy.2021.122359
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    Cited by:

    1. Meng, Zhaohui & Zheng, Haimin & Qin, Fankai & Li, Anqi & Li, Huimin & Dong, Sijie & Song, Chao & Miao, Xinyang & Yue, Wenzheng & Zhao, Kun & Zhan, Honglei, 2023. "Mechanistic study of the effect of hydrocarbon unsaturation on the distribution state of water molecules at the oil-water interface by oblique incident reflectance difference technique," Energy, Elsevier, vol. 276(C).

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