High accuracy capillary network representation in digital rock reveals permeability scaling functions

Area of Science:

• Geosciences
• Petrophysics
• Computational Fluid Dynamics

Background:

• Permeability is crucial for understanding fluid flow in porous rocks.
• Predicting permeability from pore-scale geometry is challenging due to resolution limits and scale approximations.
• Existing methods struggle with systematic upscaling of permeability predictions.

Purpose of the Study:

• To develop and validate a novel pore-scale network model for accurate permeability prediction.
• To overcome limitations in feature resolution and microscopic scale approximations in current methods.
• To establish scaling relationships between microscale properties and mesoscale permeability.

Main Methods:

• Fluid flow simulations were conducted using a novel capillary network representation with enhanced microscale spatial detail.
• The pore-scale network model was applied to various geological samples with a wide range of permeability values.
• Network-based flow simulations were compared against experimental permeability measurements.

Main Results:

• Network-based flow simulations accurately predicted experimental permeabilities without calibration or correction.
• The method demonstrated effectiveness across geological samples with permeability values spanning two orders of magnitude.
• New scaling relationships were derived, linking microscale capillary diameter and fluid velocity to mesoscale permeability.

Conclusions:

• The developed pore-scale network model offers a robust and accurate approach for predicting rock permeability.
• The findings provide fundamental insights into the emergence of mesoscale permeability from microscale characteristics.
• This work advances the understanding of fluid flow in porous media and its prediction capabilities.