Nonlinear characterization of oil–gas–water three-phase flow in complex networks

Understanding the dynamics of oil–gas–water three-phase flow has been a challenge in the fields of nonlinear dynamics and fluid mechanics. We systematically carried out oil–gas–water three-phase flow experiments for measuring the time series of flow signals, which is studied in terms of the mapping from time series to complex networks. Two network mapping methods are proposed for the analysis and identification of flow pattern dynamics, i.e. Flow Pattern Complex Network (FPCN) and Fluid Dynamic Complex Network (FDCN). Through detecting the community structure of FPCN based on K-means clustering, distinct flow patterns can be successfully distinguished and identified. A number of FDCN's under different flow conditions are constructed in order to reveal the dynamical characteristics of three-phase flows. The network information entropy of FDCN is sensitive to the transition among different flow patterns, which can be used to characterize nonlinear dynamics of the three-phase flow. These interesting and significant findings suggest that complex networks can be a potentially powerful tool for uncovering the nonlinear dynamics of oil–gas–water three-phase flows.