Characterization and reconstruction of rough fractures based on vector statistics

Wu，Mingyang1,2,3; Jiang，Changbao1; Deng，Bozhi1; Gao，Ke4; Li，Minghui5; Liu，Bo6

2024-03-01

10.1016/j.geoen.2024.212664

Geoenergy Science and Engineering   影响因子和分区

2949-8910

The characteristics of fracture networks are of great significance for unconventional geo-energy exploitation such as shale oil, shale gas and geo-thermal extraction. Natural fractures and induced fractures after reservoir stimulation control the mechanical properties and fluid flow of the reservoirs. The quantitative characterization and reconstruction of rough fractures are essential for studying the fluid flow and mechanical properties of the reservoirs with complicated fracture networks. Based on vector statistics, a vector statistical method (VSM) for quantitative characterization of rough fractures is proposed and tested using 10 standard joint profiles. Furthermore, the growth vector counting method (counting method) and growth vector probability method (probability method) for single rough fractures in two-dimensional (2D) and three-dimensional (3D) models are proposed. Afterward, a morphology comparison and quantitative evaluation of single rough fractures before and after reconstruction are conducted. When the counting and probability methods are used to reconstruct single rough fractures, the difference in the tortuosity and fractal dimensions of the original and reconstructed fractures are less than 5% and 2.5%, respectively. It implies the counting and probability methods proposed herein can reconstruct rough fractures with approximate statistical characteristics and quantitative characterization parameters. Subsequently, the counting and probability methods for single rough fractures are further developed for 2D and 3D modeling of conventional and rough discrete fracture networks. The results indicate that the growth vector counting and probability methods proposed in this study have significant potential for rough discrete fracture network modeling. In addition, the merits and limitations of the proposed algorithms in modeling discrete fracture network models are discussed.

Quantitative characterization Rough discrete fracture network Rough fractures Stochastic reconstruction Vector statistics

SCI ; EI

英语

其他

2-s2.0-85183914081

Scopus

1.State Key Laboratory of Coal Mine Disaster Dynamics and Control,School of Resources and Safety Engineering,Chongqing University,Chongqing,400030,China
2.State Key Laboratory of Geomechanics and Geotechnical Engineering,Wuhan Institute of Rock and Soil Mechanics,Chinese Academy of Science,Wuhan,430071,China
3.Hubei Key Laboratory of Geo-Environmental Engineering,Institute of Rock and Soil Mechanics,Chinese Academy of Sciences,Wuhan,430071,China
4.Department of Earth and Space Sciences,Southern University of Science and Technology,Shenzhen,518055,China
5.Guangdong Provincial Key Laboratory of Deep Earth Sciences and Geothermal Energy Exploitation and Utilization,Institute of Deep Earth Sciences and Green Energy,Shenzhen University,Shenzhen,518060,China
6.Faculty of Earth Science and Environmental Engineering,Southwest Jiaotong University,Chengdu,611756,China

Wu，Mingyang,Jiang，Changbao,Deng，Bozhi,et al. Characterization and reconstruction of rough fractures based on vector statistics[J]. Geoenergy Science and Engineering,2024,234.

Wu，Mingyang,Jiang，Changbao,Deng，Bozhi,Gao，Ke,Li，Minghui,&Liu，Bo.(2024).Characterization and reconstruction of rough fractures based on vector statistics.Geoenergy Science and Engineering,234.

Wu，Mingyang,et al."Characterization and reconstruction of rough fractures based on vector statistics".Geoenergy Science and Engineering 234(2024).