Technology Overview

Researchers at McMaster University have developed a unique tool that combines a three-dimensional CNN and a physics-informed neural network (PINN) model, which enables highly accurate, real-time and end-to-end permeability prediction of digital rocks at the core-scale (large predictable sample size).

Benefits

• Real-time analysis (Sub-second predictions)
• End-to-end analysis (Direct prediction from segmented CT images)
• Large predictable sample size (Can accommodate arbitrarily large digital rock samples)
• Accurate permeability results (Below 10% relative error)
• Easy to use (No calibration needed)