Dynamic Mode Decomposition of real-time 4D imaging data to explore intermittent fluid connectivity in subsurface flows

Abstract

The interaction of multiple fluids through a heterogeneous pore space leads to complex pore-scale flow dynamics, such as intermittent pathway flow. The non-local nature of these dynamics, and the size of the 4D datasets acquired to capture them, presents challenges in identifying key fluctuations controlling fluid connectivity. To address these challenges, this work employs Dynamic Mode Decomposition (DMD), a data-driven algorithm that decomposes complex nonlinear systems into dominant spatio-temporal structures without relying on prior system assumptions. We present a workflow that identifies critical spatio-temporal regions exhibiting intermittent flow dynamics. This workflow is validated through three test cases, each exploring the impact of viscosity ratio on flow dynamics while maintaining a constant capillary number. Our findings demonstrate DMD's potential in analyzing extensive experimental datasets and identifying crucial intermittent flow structures, offering a powerful tool for understanding complex fluid behaviors in heterogeneous pore spaces. Using our method, we can quickly identify the timescales and locations of interest in an objective manner, providing a valuable diagnostic tool for analysing large synchrotron datasets.