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

• Published in: Journal of petroleum exploration and production technology Vol. 10; no. 8; pp. 3723 - 3732
• Main Authors: Li, Zhong-Cheng, Fan, Chun-Ling
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.12.2020; SpringerOpen
• Subjects: Earth and Environmental Science; Earth Sciences; ELM; Energy Systems; Geology; IMF; Industrial and Production Engineering; Industrial Chemistry/Chemical Engineering; MEMD; Monitoring/Environmental Analysis; Normalized energy; Offshore Engineering; Oil–water two-phase flow; Original Paper-Production Engineering; Oil–water two-phase flow; IMF; MEMD; Normalized energy; ELM
• ISSN: 2190-0558; 2190-0566
• DOI: 10.1007/s13202-020-00987-1

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.