An Efficient Method for Generating a Super-Sized and Heterogeneous Pore-Throat Network Model of Rock

作     者：Yu, Chunlei Chen, Wenbin Li, Junjian Wang, Shuoliang 

作者机构：Sinopec Shengli Oilfield Branch Explorat & Dev Res Inst Dongying 257000 Peoples R China China Univ Geosci Beijing Sch Energy Resources Beijing 100083 Peoples R China China Univ Petr Sch Petr Engn Beijing 100100 Peoples R China 

出 版 物：《APPLIED SCIENCES-BASEL》 (Appl. Sci.)

年 卷 期：2025年第15卷第3期

页      面：1047-1047页

核心收录：

基　　金：National Natural Science Foundation of China 52374051 

主　　题：pore-throat network quartet structure generation set random growth partition control 

摘      要：The super-sized pore-throat network model can reflect both microscopic pore characteristics and macroscopic heterogeneity and is excellent in describing cross-scale flow fields. At present, there is no algorithm that can generate a micro pore-throat network model at a macro reservoir scale. This study examines algorithms for super-sized pore-throat network reconstruction using actual core sample data. It conducts a random simulation of mineral growth and dissolution under the constraints of four microscopic pore structure parameters: porosity, coordination number, pore radius, and throat radius. This approach achieves high-precision, super-sized, and regional pore-throat network modeling. Comparative analysis shows that these four parameters effectively guide the random growth process of super-sized pore-throat networks. The overall similarity between the generated pore-throat network model and real core samples is 88.7% on average. In addition, the algorithm can partition and control the generation of pore-throat networks according to sedimentary facies. The 100-megapixel model with 85,000 pores was generated in 455.9 s. This method can generate cross-scale models and provides a basis for cross-scale modeling in physical simulation experiments and numerical simulations.