The mechanism and quantitative descriptions of nonlinear fluid flow through rock fractures are difficult issues of high concern in underground engineering fields. In order to study the effects of fracture geometry and loading conditions on nonlinear flow properties and normalized transmissivity through fracture networks, stress-dependent fluid flow tests were conducted on real rock fracture networks with different number of intersections (1, 4, 7, and 12) and subjected to various applied boundary loads (7, 14, 21, 28, and 35 kN). For all cases, the inlet hydraulic pressures ranged from 0 to 0.6 MPa. The test results show that Forchheimer’s law provides an excellent description of the nonlinear fluid flow in fracture networks. The linear coefficient a and nonlinear coefficient b in Forchheimer’s law J=aQ+bQ2 generally decrease with the number of intersections but increase with the boundary load. The relationships between a and b can be well fitted with a power function. A nonlinear effect factor E=bQ2/(aQ+bQ2) was used to quantitatively characterize the nonlinear behaviors of fluid flow through fracture networks. By defining a critical value of E = 10%, the critical hydraulic gradient was calculated. The critical hydraulic gradient decreases with the number of intersections due to richer flowing paths but increases with the boundary load due to fracture closure. The transmissivity of fracture networks decreases with the hydraulic gradient, and the variation process can be estimated using an exponential function. A mathematical expression T/T0=1-exp(-αJ-0.45) for decreased normalized transmissivity T/T0 against the hydraulic gradient J was established. When the hydraulic gradient is small, T/T0 holds a constant value of 1.0. With increasing hydraulic gradient, the reduction rate of T/T0 first increases and then decreases. The equivalent permeability of fracture networks decreases with the applied boundary load, and permeability changes at low load levels are more sensitive.
From invasion percolation to flow in rock fracture networks
