ABSTRACT
High-volume hydraulic fracturing (HVHF) gas-drilling operations in the Marcellus Play have raised environmental concerns, including the risk of groundwater contamination. Fingerprinting water impacted by gas-drilling operations is not trivial given other potential sources of contamination. We present a multivariate statistical modeling framework for developing a quantitative, geochemical fingerprinting tool to distinguish sources of high salinity in shallow groundwater. The model was developed using new geochemical data for 204 wells in New York State (NYS), which has a HVHF moratorium and published data for additional wells in NYS and several salinity sources (Appalachian Basin brines, road salt, septic effluent, and animal waste). The model incorporates a stochastic simulation to predict the geochemistry of high salinity (>20 mg/L Cl) groundwater impacted by different salinity sources and then employs linear discriminant analysis to classify samples from different populations. Model results indicate Appalachian Basin brines are the primary source of salinity in 35% of sampled NYS groundwater wells with >20 mg/L Cl. The model provides an effective means for differentiating groundwater impacted by basin brines versus other contaminants. Using this framework, similar discriminatory tools can be derived for other regions from background water quality data.
