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• W3162133274 abstract "Numerous geochemical approaches have been proposed to ascertain if methane concentrations in groundwater, [CH 4 ], are anomalous, i.e., migrated from hydrocarbon production wells, rather than derived from natural sources. We propose a machine-learning model to consider alkalinity, Ca, Mg, Na, Ba, Fe, Mn, Cl, sulfate, TDS, specific conductance, pH, temperature, and turbidity holistically together. The model, an ensemble of sub-models targeting one parameter pair per sub-model, was trained with groundwater chemistry from Pennsylvania ( n =19,086) and a set of 16 analyses from putatively contaminated groundwater. For cases where [CH 4 ] ≥ 10 mg/L, salinity- and redox-related parameters sometimes show that CH 4 may have moved into the aquifer recently and separately from natural brine migration, i.e., anomalous CH 4 . We applied the model to validation and hold-out data for Pennsylvania ( n =4,786) and groundwater data from three other gas-producing states: New York ( n =203), Texas ( n =688), and Colorado ( n =10,258). The applications show that 1.4%, 1.3%, 0%, and 0.9% of tested samples in these four states, respectively, have high [CH 4 ] and are ≥50% likely to have been impacted by gas migrated from exploited reservoirs. If our approach is indeed successful in flagging anomalous CH 4 , we conclude that: i) the frequency of anomalous CH 4 (# flagged water samples / total samples tested) in the Appalachian Basin is similar in areas where gas wells target unconventional as compared to conventional reservoirs, and ii) the frequency of anomalous CH 4 in Pennsylvania is higher than in Texas + Colorado. We cannot, however, exclude the possibility that differences among regions might be affected by differences in data volumes. Machine learning models will become increasingly useful in informing decision-making for shale gas development. • We build a data-driven model to predict the likelihood of anomalous CH 4 in water • We apply this model to water quality data sets from PA, NY, TX, and CO • Models detect similar rates of anomalous CH 4 cases between PA and NY • Models detect more anomalous CH 4 cases in PA than in TX + CO" @default.
• W3162133274 created "2021-05-24" @default.
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• W3162133274 date "2021-07-01" @default.
• W3162133274 modified "2023-10-11" @default.
• W3162133274 title "Detecting anomalous methane in groundwater within hydrocarbon production areas across the United States" @default.
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• W3162133274 doi "https://doi.org/10.1016/j.watres.2021.117236" @default.
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