One challenge facing triggered seismicity hazard assessment is an apparent lack of readily-available, strong predictors: it is difficult or expensive to obtain the necessary data to determine whether injection at a particular site is likely to induce earthquakes. Susceptibility to induced seismic activity is influenced by a host of site- and operation-specific parameters, such as ambient stress magnitudes, presence of faulting, and the volume of fluid injection. Modern machine learning techniques can help us make sense of these multi-dimensional spaces, first, by dividing the parameter space into discrete regions of seismic susceptibility and, second, by probing the functional (predictive) relationships between predictors and outcome.
This investigation will help direct efforts to improve numerical tools for modeling induced seismicity at the reservoir scale. Modern reservoir flow simulators are equipped for modeling the injection operation, reservoir pressure build-up and associated geomechanical deformation, and fault failure criteria; however, they typically neglect the earthquakes themselves or, if capable, focus on individual triggered events. We are implementing new methods for modeling multiple earthquake events – sequences – into existing reservoir simulators. These new tools will be applied to several recent induced earthquake sequences with a focus on understanding their bulk behavior, for instance, the correlation between injection and seismicity rate, and the variable delay between the beginning of injection at a site and the subsequent onset of seismic activity (if any.)