## 3 Methods  
The conditions of the fractured reservoir were analysed using well-logs, rock specimens and vein-filling laumontite associated with the A-1 well. To characterise the penetrated interval lithologically, the standard petrographic microscopy analysis was applied. All available well logs (gamma ray--\(GR\), resistivity--\(Res\), density--\(Den\), and compensated neutron--\(CN\)) were evaluated using [PERSON] et al. 's [19] method, to localise the tectonised zones and distinguish different tectonised rock types in the Szeghalom Dome. Based on the several depth intervals for which both rock specimens and well-log data were available, [PERSON] et al. [19] derived a linear function using discriminant function analysis to discriminate the unaltered host rock from the tectonites. The optimal function was found to be \(F(1\text{-}2)=1.1*GR-0.5*Res-0.9*Den\), whose values were significantly lower for undeformed rock bodies than those typical in shear zones (Figure 4a). To further discriminate the three basic tectonic types of the Szeghalom area, two functions are required, with the followings being derived for this purpose: \(F3=0.7*CN-0.5*Res\) and \(F4=0.6*Den-0.9*GR\). Using these two discriminating variables, cataclasites, fault breccias, and fault gouges can be used to define independent clouds on an appropriate plot (Figure 4b). In the present study, these functions were applied to the case of A-1 well, and the results were compared with those previously obtained for the neighbouring wells. Figure 3: Exhumation and subsidence history of the study area revealed by [PERSON] et al. [22] and [PERSON] et al. [18]. To simulate the fracture network, the typical geometric parameters were first measured along the well, using high-resolution core images. In the first step, the position of each fracture was recorded along a reference line, resulting in a series of intersection points. This point series was evaluated to derive three fracture density parameters: the spacing (inter-point distance), P10 index and fractal dimension of spacing. The spacing log exhibits the variation in distances between any two neighbouring fracture intersections along a well, and thus visualises the varying density in segments of the fracture network. The frequently used parameter P10 [24] measures the number of fractures along any 1-m-long interval. In contrast to the spacing, P10 increases in more intensely fractured intervals. For the simulation, a third parameter is worth deriving. As fracture networks are usually handled as 3D fractal-type geometries [25; 26], the intersection of the network with any line results in a fractal pattern embedded in one dimension [27]. There are numerous methods for computing the fractal dimension of such a point series, such as box counting, roughness length analysis, and special variogram analysis [28]. [PERSON] [28] introduced a multi-step approach for using the rescaled range (R/S) analysis to calculate the fractal dimension (\(D\)) of any segment of a fracture network crosscut by a bore core. Using this approach, a \(D\)-log can also be constructed along the well and compared with the two other fracture density logs. In this paper the [PERSON]'s [29] algorithm to 6-m-long overlapping intervals was applied. In this way, the \(D\)-value can be calculated every 3 m.  
The fracture length data of a fracture system in metamorphic rocks usually follow a power-law distribution through numerous scales, ranging from centimetres to tens of metres [30; 31]. That is, N(\(L\)) = F * \(L^{-\text{E}}\), where \(L\) is the trace length of a fracture on any 2D section, N is the number of fractures of length \(L\) and E (the length exponent) and F are constants. By determining the trace length of several individual fractures, their distribution function can be computed, and thus, E and F can be computed. To avoid unpredictable bias in this estimation, only those fractures that terminated inside the bore core were used in the image analysis.