Fuzzy inference system application for oil-water flow patterns identification

Yuyan Wu, Haimin Guo, Hongwei Song and Rui Deng

Energy, 2022, vol. 239, issue PD

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.

Keywords: Production logging; Oil-water two-phase flow; Fuzzy inference system; Non-vertical well (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:239:y:2022:i:pd:s0360544221026086

DOI: 10.1016/j.energy.2021.122359

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