Modality transition-based network from multivariate time series for characterizing horizontal oil–water flow patterns

Abstract

The simultaneous°ow of oil and water through a horizontal pipe is a common occurrence during petroleum industrial processes. Characterizing the°ow behavior underlying horizontal oil-water°ows is a challenging problem of signi¯cant importance. In order to solve this problem, we carry out experiment to measure multivariate signals from di®erent°ow patterns and then propose a novel modality transition-based network to analyze the multivariate signals. The results suggest that the local betweenness centrality and weighted shortest path of the constructed network can characterize the transitions of°ow conditions and further allow quantitatively distinguishing and uncovering the dynamic°ow behavior underlying di®erent horizontal oil-water°ow patterns.