Pore-network extraction from micro-computerized-tomography images

TLDR

Network models that represent the void space of a rock by a lattice of pores connected by throats can predict relative permeability once the pore geometry and wettability are known, but micro‑CT images cannot be directly input into such models. The study develops a modified maximal‑ball algorithm to extract simplified pore‑throat networks with parametrized geometry and interconnectivity from micro‑CT images. The algorithm computes network parameters such as coordination number and pore and throat size distributions, and compares them to benchmark data from other extraction methods, experimental measurements, and direct permeability calculations. The extracted networks show good agreement with benchmark data in most cases, enabling their use for predictive modeling across a wide variety of rock types.

Abstract

Network models that represent the void space of a rock by a lattice of pores connected by throats can predict relative permeability once the pore geometry and wettability are known. Micro-computerized-tomography scanning provides a three-dimensional image of the pore space. However, these images cannot be directly input into network models. In this paper a modified maximal ball algorithm, extending the work of Silin and Patzek [D. Silin and T. Patzek, Physica A 371, 336 (2006)], is developed to extract simplified networks of pores and throats with parametrized geometry and interconnectivity from images of the pore space. The parameters of the pore networks, such as coordination number, and pore and throat size distributions are computed and compared to benchmark data from networks extracted by other methods, experimental data, and direct computation of permeability and formation factor on the underlying images. Good agreement is reached in most cases allowing networks derived from a wide variety of rock types to be used for predictive modeling.