Pore-network extraction using discrete Morse theory: Preserving the topology of the pore space

Zubov, A.S.; Murygin, D.A.; Gerke, K.M.

Авторы: Zubov Andrey S., Murygin Dmitry A., Gerke Kirill M.
Журнал: Physical Review E - Statistical, Nonlinear, and Soft Matter Physics
Том: 106
Номер: 5
Год издания: 2022
Издательство: American Physical Society
Местоположение издательства: United States
Номер статьи: 055304
DOI: 10.1103/physreve.106.055304
Аннотация: Pore-scale modeling based on the 3D structural information of porous materials has enormous potential in assessing physical properties beyond the capabilities of laboratory methods. Such capabilities are pricey in terms of computational expenses, and this limits the applicability of the direct simulations to a small volume and requires high-performance computational resources, especially for multiphase flow simulations. The only pore-scale technique capable of dealing with large representative volumes of porous samples is pore-network (PNM) based modeling. The problem of the PNM approach is that 3D pore geometry first needs to be simplified into a graph of pores and throats that conserve topological and geometrical properties of the original 3D image. While significant progress has been achieved in terms of geometry representation, no methodology provides full conservation of the topological features of the pore structure. In this paper we present a pore-network extraction algorithm for binary 3D images based on discrete Morse theory and persistent homology that by design targets topology preservation. In addition to methodological developments, we also clarify the relationship between topological characteristics of constructed Morse chain complex and pore-network elements. We show that the Euler numbers calculated for PNMs based on our methodology coincide with those obtained using the direct topological analysis. The characteristics of the extracted pore network are calculated for several 3D porous binary images and compared with the results of maximum inscribed balls-based and watershed-based approaches as well as a hybrid approach to support our methodology.
Добавил в систему: Герке Кирилл Миронович

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