Flow in multi-scale discrete fracture networks with stress sensitivity

For hydraulic fracturing in the shale reservoir, the existence of natural fractures has a great impact on the propagation of hydraulic fractures and the flow capacity of porous media. Our previous study mainly discussed the effect of different fracture parameters (i.e., orientation, aperture) on the fracture network permeability based on discrete fracture model and finite elements analyses (Liang et al., 2016). To further account for the in-situ condition, we now investigate the effect of stress sensitivity, i.e. the stress difference between hydraulic fractures and natural fractures, on flow behavior in multi-scale discrete fracture networks. A series of sensitivity experiments on both naturally and artificially fractured cores with the existence of proppants were carried out. Fracture models at different scale were established based on the corresponding mathematical fitting models, to study the effect of stress sensitivity on the seepage process. Results show that with the increase of confining pressure, the permeability of natural fractures decreases exponentially while the permeability of hydraulic fractures decreases more slowly following the cubic polynomial law. The stress sensitivity of natural fractures has more influence on the flow dynamics than that of hydraulic fractures, and this difference is subject to the fracture orientation, fracture length, and intersection relationship. When the fracture orientation is parallel to pressure gradient or the fracture can serve as part of the main channel, the stress sensitivity has great impact on the productivity of the fracture network. The drainage area around the hydraulic fractures are almost the same irrespective of the stress, but the drainage area of natural fractures changes significantly when considering stress sensitivity. Therefore, it is necessary to incorporate the stress effect on the flow conductivity of hydraulic and natural fractures while modeling complex fractured reservoirs. (C) 2016 Elsevier B.V. All rights reserved.