Geothermal energy is becoming an important clean energy source, however, the stimulation of a reservoir for an Enhanced Geothermal System (EGS) is associated with seismic risk due to induced seismicity. Seismicity occurring due to the water injection at depth have to be well recorded and monitored. To mitigate the seismic risk of a damaging event, an appropriate alarm system needs to be in place for each individual experiment. In recent experiments, the so-called traffic-light alarm system, based on public response, local magnitude and peak ground velocity, was used. We aim to improve the pre-defined alarm system by introducing a probability-based approach; we retrospectively model the ongoing seismicity in real time with multiple statistical forecast models and then translate the forecast to seismic hazard in terms of probabilities of exceeding a ground motion intensity level. One class of models accounts for the water injection rate, the main parameter that can be controlled by the operators during an experiment. By translating the models into time-varying probabilities of exceeding various intensity levels, we provide tools which are well understood by the decision makers and can be used to determine thresholds non-exceedance during a reservoir stimulation; this, however, remains an entrepreneurial or political decision of the responsible project coordinators. We introduce forecast models based on the data set of an EGS experiment in the city of Basel. Between 2006 December 2 and 8, approximately 11 500 m3 of water was injected into a 5-km-deep well at high pressures. A six-sensor borehole array, was installed by the company Geothermal Explorers Limited (GEL) at depths between 300 and 2700 m around the well to monitor the induced seismicity. The network recorded approximately 11 200 events during the injection phase, more than 3500 of which were located. With the traffic-light system, actions where implemented after an ML 2.7 event, the water injection was reduced and then stopped after another ML 2.5 event. A few hours later, an earthquake with ML 3.4, felt within the city, occurred, which led to bleed-off of the well. A risk study was later issued with the outcome that the experiment could not be resumed. We analyse the statistical features of the sequence and show that the sequence is well modelled with the Omori–Utsu law following the termination of water injection. Based on this model, the sequence will last 31+29/−14 years to reach the background level. We introduce statistical models based on Reasenberg and Jones and Epidemic Type Aftershock Sequence (ETAS) models, commonly used to model aftershock sequences. We compare and test different model setups to simulate the sequences, varying the number of fixed and free parameters. For one class of the ETAS models, we account for the flow rate at the injection borehole. We test the models against the observed data with standard likelihood tests and find the ETAS model accounting for the on flow rate to perform best. Such a model may in future serve as a valuable tool for designing probabilistic alarm systems for EGS experiments.