Stress-Based and Convolutional Forecasting of Injection-Induced Seismicity: Application to The Otaniemi Geothermal Reservoir Stimulation
Induced seismicity observed during Enhanced Geothermal Stimulation (EGS) at Otaniemi, Finland is modelled using both statistical and physical approaches. The physical model produces simulations closest to the observations when assuming rate-and-state friction for shear failure with diffusivity matching the pressure build-up at the well-head at onset of injections. Rate-and-state friction implies a time dependent earthquake nucleation process which is found to be essential in reproducing the spatial pattern of seismicity. This implies that permeability inferred from the expansion of the seismicity triggering front (Shapiro, 1997) can be biased. We suggest a heuristic method to account for this bias that is independent of the earthquake magnitude detection threshold. Our modelling suggests that the Omori law decay during injection shut-ins results mainly from stress relaxation by pore pressure diffusion. During successive stimulations, seismicity should only be induced where the previous maximum of Coulomb stress changes is exceeded. This effect, commonly referred to as the Kaiser effect, is not clearly visible in the data from Otaniemi. The different injection locations at the various stimulation stages may have resulted in sufficiently different effective stress distributions that the effect was muted. We describe a statistical model whereby seismicity rate is estimated from convolution of the injection history with a kernel which approximates earthquake triggering by fluid diffusion. The statistical method has superior computational efficiency to the physical model and fits the observations as well as the physical model. This approach is applicable provided the Kaiser effect is not strong, as was the case in Otaniemi.