According to the energy strategy of Switzerland for the year 2050, deep geothermal technologies like Enhanced Geothermal Systems (EGS) need to provide approximately 7% of Switzerland's electricity supply. In order for this strategic goal to be reached, not only further fundamental and applied researches are required, but also several stakeholders such as the investors, the local community and the regulatory authorities should harmoniously collaborate. Important decisions that are related to induced seismicity, need to be made at periods of high uncertainty and need to be as mutually acceptable as possible. Computational tools are currently developed by the Swiss Seismological Service (SED) that integrate over the space of this uncertainty and aim to return forecasts of the efficacy of a stimulation strategy for a number of different criteria. Probabilistic long term forecasts of EGS operations are obtained by performing Monte Carlo simulations both for the creation and the production phase. The former is important for probabilistically assessing the induced seismicity hazard (e.g. in a real-time traffic light application) and the latter for assessing the commerciality of a stimulation strategy. Each realization of the Monte Carlo simulation considers a different but still probable sequence of induced seismicity events that has been sampled according to observed statistical field observations and to how pore-pressure is expected to have evolved for the modeled sequence of events. Pore-pressure and temperature propagations are modeled with HFR-Sim, which is the in-house EGS simulator that employs a 3D embedded discrete fracture model for modeling flow and heat transport inside an EGS reservoir. HFR-Sim has been especially designed for such dynamically changing fracture networks, it can simulate a wide range of probable EGS scenarios in reasonable return times, and the hydraulic effect of many induced seismicity related field observations can be quickly approximated. Here, we focus on numerical recipes and simplifications that significantly prolong the forecasting ability. An exemplary Monte Carlo simulation is presented and discussed.

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