A Probabilistic Approach for Predicting Methane Occurrence in Groundwater.

Environ Sci Technol

Applied Geochemistry Group, Department of Geoscience , University of Calgary , 2500 University Dr. NW, Calgary , Alberta T2N 1N4 , Canada.

Published: November 2019

AI Article Synopsis

  • Aqueous geochemistry datasets from groundwater monitoring programs are valuable for environmental baseline assessments, particularly in areas with shale gas development.
  • A logistic regression model was created to predict methane occurrence in Alberta's aquifers, demonstrating high accuracy in predicting methane presence from two monitoring programs.
  • The model uses basic hydrochemical data to fill gaps in methane concentration information, enhancing environmental assessments in regions lacking specific groundwater gas data.

Article Abstract

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% ( = 234 samples) and 88.1% ( = 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 ( = 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.

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Source
http://dx.doi.org/10.1021/acs.est.9b03981DOI Listing

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