A novel method to identify the flow pattern of oil–water two-phase flow

• Main Authors: Zhong-Cheng Li, Chun-Ling Fan
• Format: Article
• Language: English
• Published: SpringerOpen 2020-09-01
• Series: Journal of Petroleum Exploration and Production Technology
• Subjects: Oil–water two-phase flow; ELM; MEMD; IMF; Normalized energy
• Online Access: https://doi.org/10.1007/s13202-020-00987-1

Abstract This paper presents a novel method combining extreme learning machine (ELM) and multiple empirical mode decomposition (MEMD) to identify flow patterns of oil–water two-phase flow. The proposed method can recognize accurately five typical flow patterns of horizontal oil–water two-phase flow. Taking the Lorenz system as an example, we verify the MEMD is more suitable for simultaneous decomposition of multi-channel signals than empirical mode decomposition and ensemble empirical mode decomposition. In the proposed method, we employ the MEMD to decompose the multivariate conductance signal of oil–water two-phase flow to obtain the same intrinsic mode function modes, select the normalized energy of the high-frequency components as the eigenvalue, and utilize the trained ELM to achieve a good recognition result. The experimental results show that the proposed method is not only fast and generalized, but also has high accuracy in identifying flow patterns of oil–water two-phase flow.