Some of these studies include the modeling and simulation of the physical processes whereas some studies delve into the statistical analyses of the relationships between disposal wells and induced seismicity. However, most of the previous attempts on the statistical analysis of this dataset use a more qualitative view of the problem (Langenbruch & Zoback, 2016) instead of quantifying the impact of various parameters such as injection rate, volumes, pressures, etc. Other work like Gogri et al. (2017) uses geo-modeling and simulation approach, but this constraint the modeling to a smaller section due to limitations on the seismic data extent.

In this work, we use various data analytics methods to quantify the impact of different injection well parameters and rock properties on the earthquake event magnitude and intensity. Our models show that hierarchical and K-means clustering are able to group the wells into clusters that conforms with the earthquake event density.

Including more clusters in our analysis refined the results but in general, four clusters are enough to capture the trends in our dataset. Fuzzy clustering, which is a soft clustering yields good results only after number of clusters exceed five.