Construction of 3D Discrete Fracture Network Model using Structural and Hydrogeological Data

<p>The presence of fractures and discontinuities in the intact rock affects the hydraulic, thermal, chemical and mechanical behavior of the underground structures. Various techniques have been developed to provide information on the spatial distribution of these complex features. LIDAR, for instance, could provide a 2D fracture network model of the outcrop, Geophysical borehole logs such as OPTV and ATV can be used to investigate 1D geometrical data (i.e. dip and dip direction, aperture) of the intersected fractures, and seismic survey can mainly offer a large structure distribution of the deep structures. The ability to combine all the existing data collected from various resources and different scales to construct a 3D discrete fracture network (DFN) model of the rock mass allows to adequately represent the physical behavior of the interested subsurface structure.</p><p>In this study, an effort on the construction of such a 3D DFN model is carried out via combination of various structural and hydrogeological data collected in fractured crystalline rock. During the pre-characterization phase of the In-situ Stimulation and Circulation (ISC) experiment [Amann et al., 2018] at the Grimsel Test Site (GTS) in central Switzerland, a comprehensive characterization campaign was carried out to better understand the hydromechanical characteristics of the existing structures. The collected multiscale and multidisciplinary data such as OPTV, ATV, hydraulic packer testing and solute tracer tests [Jalali et al., 2018; Krietsch et al., 2018] are combined, analyzed and interpreted to form a combined stochastic and deterministic DFN model using the FracMan software [Golder Associates, 2017]. For further validation of the model, the results from in-situ hydraulic tests are used to compare the simulated and measured hydraulic responses, allowing to evaluate whether the simulated model could reasonably represent the characteristics of the fracture network in the ISC experiment.</p><p> </p><p><strong>References</strong></p><ul><li>Amann, F., Gischig, V., Evans, K., Doetsch, J., Jalali, M., Valley, B., Krietsch, H., Dutler, N., Villiger, L., Brixel, B., Klepikova, M., Kittilä, A., Madonna, C., Wiemer, S., Saar, M.O., Loew, S., Driesner, T., Maurer, H., Giardini, D., 2018. The seismo-hydromechanical behavior during deep geothermal reservoir stimulations: open questions tackled in a decameter-scale in situ stimulation experiment. Solid Earth 9, 115–137.</li> <li>Golder Associates, 2017. FracMan User Documentation.  Golder Associates Inc, Redmond WA.</li> <li>Krietsch, H., Doetsch, J., Dutler, N., Jalali, M., Gischig, V., Loew, S., Amann, F., 2018. Comprehensive geological dataset describing a crystalline rock mass for hydraulic stimulation experiments. Scientific Data 5, 180269.</li> <li>Jalali, M., Klepikova, M., Doetsch, J., Krietsch, H., Brixel, B., Dutler, N., Gischig, V., Amann, F., 2018. A Multi-Scale Approach to Identify and Characterize the Preferential Flow Paths of a Fractured Crystalline Rock. Presented at the 2<sup>nd</sup> International Discrete Fracture Network Engineering Conference, American Rock Mechanics Association.</li> </ul>