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

TL;DR

This paper introduces a highly accurate method for simulating fluid flow in digital representations of porous rocks, enabling precise permeability predictions and revealing how microscale features influence mesoscale permeability.

Contribution

The study presents a novel capillary network modeling approach that overcomes previous resolution limitations, allowing for direct permeability prediction without calibration.

Findings

Permeability predictions match experimental measurements without calibration.

Scaling laws link microscopic pore features to mesoscale permeability.

Method applies across diverse geological samples with wide permeability range.

Abstract

Permeability is the key parameter for quantifying fluid flow in porous rocks. Knowledge of the spatial distribution of the connected pore space allows, in principle, to predict the permeability of a rock sample. However, limitations in feature resolution and approximations at microscopic scales have so far precluded systematic upscaling of permeability predictions. Here, we report fluid flow simulations in capillary network representations designed to overcome such limitations. Performed with an unprecedented level of accuracy in geometric approximation at microscale, the pore scale flow simulations predict experimental permeabilities measured at lab scale in the same rock sample without the need for calibration or correction. By applying the method to a broader class of representative geological samples, with permeability values covering two orders of magnitude, we obtain scaling relationships that reveal how mesoscale permeability emerges from microscopic capillary diameter and fluid velocity distributions.