Discrete Fracture Network Simulation of Shale Reservoir
DOI:
https://doi.org/10.54691/xcqyxc20Keywords:
Shale Reservoir; Geometric Parameters; Natural Fractures; Discrete Fracture Network.Abstract
Shale reservoirs are characterized by low porosity, low permeability, and extensive natural fractures, where fluid migration and storage primarily depend on complex fracture networks. As the primary flow channels in shale reservoirs, natural fractures not only directly govern hydrocarbon migration and distribution but also significantly influence the expansion of hydraulic fracturing fracture networks and fracturing effectiveness. Given the pronounced heterogeneity, anisotropy, and fractal characteristics of shale fractures, traditional equivalent continuum models struggle to accurately describe their intricate geometry and flow behavior. This study employs a discrete fracture network (DFN) simulation method to model spatial parameters including fracture orientation, inclination angles, and lengths. The results provide a realistic representation of natural fracture distribution in shale reservoirs, offering enhanced structural foundations for reservoir evaluation, fracturing design, and flow simulation. Therefore, DFN modeling of natural fracture systems in shale reservoirs serves dual purposes: it provides crucial insights into storage mechanisms and flow processes, while also constituting a pivotal element for optimizing shale hydrocarbon development efficiency and fracturing operations.
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[1] Li G, Zhu R. Progress, challenges and key issues of unconventional oil and gas development of CNPC[J]. China Petroleum Exploration, 2020, 25(2): 1-13.
[2] Li Y, Huang R. Relationship between joint roughness coefficient and fractal dimension of rock fracture surfaces[J]. International Journal of Rock Mechanics and Mining Sciences,2015, 7515-22.
[3] Ye, D., Liu, G., Gao. et al F. A multi-field coupling model of gas flow in fractured coal seam. Advances in Geo-Energy Research, 2021, 5(1): 104-118.
[4] Panton B, Elmo D, Stead D, et al. A discrete fracture network approach for the design of rock foundation anchorage[J]. Mining Technology,2015,124(3):150-162.
[5] Mohajerani S, Wang G, Huang D, et al. An Efficient Computational Model for Simulating Stress-dependent Flow in Three-dimensional Discrete Fracture Networks[J]. Ksce Journal of Civil Engineering,2019,23(3):1384-1394.
[6] Leung O T C, Zimmerman W R. Estimating the Hydraulic Conductivity of Two-Dimensional Fracture Networks Using Network Geometric Properties[J]. Transport in Porous Media,2012,93(3):777-797.
[7] Xue K, Zhang Z, Hao S, et al. On the onset of nonlinear fluid flow transition in rock fracture network: Theoretical and computational fluid dynamic investigation. Physics of Fluids. 2022 ;34(12).
[8] Dreuzy D J, Davy P, Bour O. Hydraulic properties of two‐dimensional random fracture networks following a power law length distribution: 1. Effective connectivity[J]. Water Resources Research, 2001, 37(8):2065-2078.
[9] Zhang H. Study on the evolution mechanism of intensive cutting volumetric fracturing fracture network in continental shale oil reservoirs[D]. Xi’an Shiyou University, 2023.
[10] Pan D, Li S, Xu Z, et al. A deterministic-stochastic identification and modelling method of discrete fracture networks using laser scanning: Development and case study. Engineering Geology, 2019, 262: 105310.
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