## 3 Methods  
Fracture networks in rocks are fractal-like objects that can be modelled at any scale based on the geometric parameters of the individual fractures that make up the fracture network [35; 36]. This study used discrete fracture network (DFN) modelling to investigate the differences between fracture networks of different host rocks [37]. Primary data for modelling are the geometric parameters of individual fractures, i.e., fracture length distribution, spatial density, the orientation of fractures and their aperture. Based on these geometric parameters, a discrete fracture network was created using the Infress software [38; 39; 40]. Fracture length is an essential geometric parameter of DFN modelling. Numerous studies have shown that the distribution of fracture lengths follows a power-law function so that  
\[\text{N(L)}=\text{F}\times\text{L}^{-\text{E}} \tag{1}\]  
Figure 3: Transmissivity log of the BAF–3 well based on well-hydraulic testing, dashed line denote the KSF–BCF border [34]. where N(L) is the number of fractures of length L, and E and F are the parameters of the distribution function [41; 42]. This asymmetric distribution means that the number of small fractures is significantly larger than the number of large fractures. Measurements of fracture lengths were performed according to the [PERSON]. [PERSON] [39] method, which requires two images, using two methods with different sensitivities. The number of fractures detected with the less sensitive way is necessarily less than the number of fractures seen with the more sensitive method, with a constant difference (\(\Delta\)N(L)). Based on this difference and the detection limits of the methods used, the parameters of Equation (1) can be calculated. For further details, see [39]. The length exponent was determined using core images (higher resolution, 1 mm) and BHTV data (lower resolution, 3 mm). The fractal dimension defines the spatial density of the fracture network. In a borehole, the fracture network is intersected by a line, resulting in a series of points. In this case, the spatial density of the fractures can be characterised by calculating the fractal dimension of the point series. Fractal dimension was calculated at every 50 m interval with an overlap of 25 m along the well, using Benoit 1.0 software [39]. The parameter P10 was also used to determine the fracture density, which is the number of intersections per metre. This parameter was also calculated for every 50 m. The orientations of the fractures were characterised by the dip direction and the dip angle obtained from the BHTV data. Infress fracture network modelling software simulates individual penny-shaped fractures in a stochastic manner using the given geometric parameters of the fracture network. It generates the desired number of equally probable realisations of the geometry of the fracture network and allows defining of the size and spatial position of the communicating subsystems of the simulated fracture networks [39; 43; 44]. The fracture aperture is an essential parameter for evaluating the hydraulic conductivity behaviour of a fractured rock body. There are several ways to determine the aperture of a fracture. The physical aperture is defined as the average distance between the opposing walls of the fracture, which can be physically measured at any scale. The aperture of a natural fracture is influenced by the prevailing stress field, the orientation of the fracture, the depth and the roughness of the fracture wall. The aperture can be altered by water-rock interaction processes, which may affect the original aperture differently from part-to-part along the fracture plane. For understanding hydrodynamic processes, on the other hand, the hydraulic aperture should be used, which is defined as the width of a theoretical pipe that produces the same flow rate as the actual fracture does [45; 46].