A Probabilistic Approach for Predicting Methane Occurrence in Groundwater

Aqueous geochemistry datasets from regional groundwater monitoring programs can be a major asset for environmental baseline assessment (EBA) in regions with development of natural gases from unconventional hydrocarbon resources. However, they usually do not include crucial parameters for EBA in areas of shale gas development such as methane concentrations. A logistic regression (LR) model was developed to predict the probability of methane occurrence in aquifers in Alberta (Canada). The model was calibrated and tested using geochemistry data including methane concentrations from two groundwater monitoring programs. The LR model correctly predicts methane occurrence in 89.8% (n = 234 samples) and 88.1% (n = 532 samples) of groundwater samples from each monitoring program. Methane concentrations strongly depend on the occurrence of electron donors such as sulfate and to a lesser extent on well depth and the total dissolved solids of groundwater. The model was then applied to a province-wide public health groundwater monitoring program (n = 52,849 samples) providing aqueous geochemistry data but no methane concentrations. This approach allowed the prediction of methane occurrence in regions where no groundwater gas data are available, thereby increasing the resolution of EBA in areas of shale gas development by using basic hydrochemical parameters measured in high-density groundwater monitoring programs.