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the ARC Training Centre for Multiscale 3D Imaging, Modelling and Manufacturing

P4-2. Time-lapse 3D Tomography to Understand Dynamics and Kinetics via Fluid-fluid Interface Evolution in Porous Media

Project leader: Anna Herring (Dept. Applied Maths, RSPhys, ANU)
Industry partner: Pål-Eric Øren, Petricore
Fig. 1: (Zoom window on top) CO₂ contact angles, interface pinning/movement → rock wettability and energy dissipation Δt = ms - sec;
(middle) Brine-CO₂ interfacial areas and ganglia volumes → solubility kinetics Δt = hours - days;
(bottom) Ganglia Curvature → Ostwald Ripening Δt = weeks - months.
Objective:
  1. Predict transport and ultimate long-term fate of CO₂ injected during geologic CO₂ sequestration operations.
  2. Quasi-static (“timelapse”) CT imaging will be used to characterize dynamic interfacial properties of CO₂ and brine flow experiments in rocks.
  3. Microscale characteristics (interfacial area, ganglia volumes, contact angle, curvature of interfaces) dictate flow and mass transfer processes on large spatial and temporal scales.
Alignment within M3D Innovation:
  1. Develop workflow for quasi-static/timelapse CT imaging over wide range of time scales.
  2. Implement new image processing algorithms (contact angles/wettability characterization; dynamic imaging artifacts).
  3. Application for both higher-speed synchrotron imaging and lower-speed CTLab imaging.
Approach:
  1. Design supercritical-condition experiments to target specific dynamic processes: wettability controls on CO₂ interface movements; solubility kinetics as a function of changing pressure, temperature, salinity, interfacial area; Ostwald ripening (monitored via curvature evolution) under “equilibrium” conditions.
  2. Execute “quasi-static” tomographic experiments to capture time-lapse 3D series (ANU CTLab or synchrotron).
  3. Innovate image analysis protocols to accurately quantify micro-scale properties (incl. programming new quantification and dynamic artifact removal algorithms).
  4. Identify quantitative links between microscale parameters and dynamic processes.
Key Milestones:
  1. Design of experiments with consideration to acquisition timeframes, process thermodynamics and kinetics, unavoidable image artifacts.
  2. Implementation/application of new and existing image analysis algorithms.
  3. Execution of experiments at synchrotron or ANU CTLab.
  4. Quantify links to dynamic processes and cross-validate (numerically) with industry partner.