Examining the capability of statistical models to mitigate induced seismicity during hydraulic fracturing of shale gas reservoirs

In this paper we test the ability of statistical methods to estimate the expected size of the largest event during stimulation, applying these approaches to two datasets collected during hydraulic stimulation of a North American Devonian Shale. We apply these methods in a prospective manner: using the microseismicity recorded during the early phases of a stimulation stage to make forecasts about what will happen as the stage continues. We do so to put ourselves in the shoes of an operator or regulator, where decisions must be taken based on data as it is acquired, rather that a post hoc analysis once a stimulation stage has been completed. We find that the proposed methods are able to provide a reasonable forecast of the largest event to occur during each stage. This means that these methods can be used as the basis of a mitigation strategy. Applying such a strategy to our case studies, we find that the majority of stages would have been allowed to continue as planned, while that the need for mitigation would have been identified for all of the stages that ended up inducing larger events.