Complex network analysis of phase dynamics underlying oil-water two-phase flows.

Area of Science:

• Fluid Dynamics
• Multiphase Flow
• Sensor Technology

Background:

• High water cut and low velocity oil-water flows present complex characterization challenges.
• Understanding these flow patterns is crucial for optimizing oil recovery and processing.
• Existing methods may lack the resolution to capture intricate phase dynamics.

Purpose of the Study:

• To develop and validate a new method for characterizing oil-water flow patterns.
• To investigate the phase dynamics governing flow transitions in high water cut, low velocity regimes.
• To provide quantitative insights into complex fluid behaviors using advanced network analysis.

Main Methods:

• Design and implementation of a high-speed cycle motivation conductance sensor for local flow measurement.
• Application of multivariate time-frequency analysis to identify flow pattern features based on energy and frequency.
• Inference of complex networks using multi-channel measurements and phase lag index to analyze phase dynamics.
• Characterization of network properties using spectral radius and weighted clustering coefficient entropy.

Main Results:

• Successfully measured local flow information across different oil-in-water flow patterns.
• Identified distinct energy and frequency features for three characteristic flow patterns.
• Revealed quantitative insights into the phase dynamics governing flow transitions and evolution.
• Demonstrated the effectiveness of network analysis in characterizing complex flow behaviors.

Conclusions:

• The developed sensor and analysis framework offer a powerful tool for studying challenging oil-water flows.
• The approach provides quantitative understanding of phase dynamics in high water cut, low velocity scenarios.
• This research contributes to improved modeling and management of multiphase flow systems.