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Updated: Apr 25, 2026
08:38
Microfluidic Devices for Characterizing Pore-scale Event Processes in Porous Media for Oil Recovery Applications
Published on: January 16, 2018
10.3K
Synthetic dataset of pore scale multiphase flow from direct numerical simulations
Unais Ashraf1, Saideep Pavuluri2, Mohammed Ishaq3
1Texas A&M University at Qatar, Chemical Engineering, Education City, Doha, Qatar.
Scientific Data
|April 23, 2026
Summary
This study simulates multiphase fluid flow in porous media to generate a comprehensive dataset. The findings aid in training machine learning models for subsurface engineering applications.
Area of Science:
- Geosciences
- Fluid Dynamics
- Porous Media Physics
Background:
- Multiphase fluid flow in pore spaces is critical for subsurface engineering.
- Accurate simulations are needed to understand fluid displacement physics.
Purpose of the Study:
- To generate a high-fidelity pore-scale dataset of two-phase flow.
- To investigate the impact of varying viscosity ratios and contact angles on flow patterns.
Main Methods:
- Conducted high-fidelity two-phase flow simulations using direct numerical simulations.
- Simulated flow across six distinct porous media with varying permeability.
- Explored nine viscosity ratios and ten contact angles, totaling 540 simulations.
Main Results:
- Generated a comprehensive dataset detailing flow patterns, pressure, and velocity variations.
- The dataset captures spatial and temporal dynamics within pore spaces.
- Data is available on Zenodo for public access and use.
Conclusions:
- The dataset provides crucial data for training machine learning algorithms.
- Serves as a benchmark for validating computationally efficient flow prediction tools.
- Enhances understanding of fluid displacement in complex subsurface environments.
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