New correlations to estimate the rough fracture permeability using computational fluid dynamics simulation

• Published in: Journal of petroleum exploration and production technology 2024-07, Vol.14 (7), p.1963-1977
• Main Authors: Valizadeh, Farshid, Ghaedi, Mojtaba, Hemmati, Sara, Feilizadeh, Mehrzad, Garmsiri, Hamid
• Format: Article
• Language: English
• Subjects: Computational fluid dynamics; Correlation; Earth and Environmental Science; Earth Sciences; Energy Systems; Fluid dynamics; Fluid flow; Fracture permeability; Fracture surfaces; Fractured reservoirs; Geology; Hydrodynamics; Independent variables; Industrial and Production Engineering; Industrial Chemistry/Chemical Engineering; Local cubic law; Monitoring/Environmental Analysis; Offshore Engineering; Original Paper-Production Engineering; Permeability; Porosity; Reservoirs; Response surface methodology; Statistical analysis; Statistical methods; Surface roughness
• ISSN: 2190-0558; 2190-0566
• DOI: 10.1007/s13202-024-01794-8

In fractured reservoirs, the fracture network provides the main path for fluid flow. Appropriate estimation of the fracture permeability influences the precise prediction of the reservoir’s future performance. Commonly, for a known geometry of natural or induced fracture, the permeability is estimated by applying local cubic law. One major drawback of this approach is that the fracture surface roughness, which has a significant effect on fracture permeability, is not considered. Moreover, the knowledge about the impact of fracture surface roughness on fracture permeability is not currently sufficient. In this research, the fluid flow in fractures with rough-walled surfaces was studied using computational fluid dynamics. For this purpose, the fluid flow through fractures was simulated by applying appropriate roughness for fracture walls. Furthermore, two correlations, based on response surface methodology and power-law models, were proposed to predict fracture permeability as a function of four independent variables (surface roughness, fracture aperture, angle, and porosity). The results of the two presented correlations were validated, and the statistical analysis indicates that both models are appropriate to predict fracture permeability. The findings of this study will be of great assistance with understanding and characterization of the fluid flow in rough fractures and can be used in future works.