Detecting anomalous methane in groundwater within hydrocarbon production areas across the United States.

Numerous geochemical approaches have been proposed to ascertain if methane concentrations in groundwater, [CH4], 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 [CH4] ≥ 10 mg/L, salinity- and redox-related parameters sometimes show that CH4 may have moved into the aquifer recently and separately from natural brine migration, i.e., anomalous CH4. 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 [CH4] and are ≥50% likely to have been impacted by gas migrated from exploited reservoirs. If our approach is indeed successful in flagging anomalous CH4, we conclude that: i) the frequency of anomalous CH4 (# 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 CH4 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.

Big Groundwater Data Sets Reveal Possible Rare Contamination Amid Otherwise Improved Water Quality for Some Analytes in a Region of Marcellus Shale Development.

Eleven thousand groundwater samples collected in the 2010s in an area of Marcellus shale-gas development are analyzed to assess spatial and temporal patterns of water quality. Using a new data mining technique, we confirm previous observations that methane concentrations in groundwater tend to be naturally elevated in valleys and near faults, but we also show that methane is also more concentrated near an anticline. Data mining also highlights waters with elevated methane that are not otherwise explained by geologic features. These slightly elevated concentrations occur near 7 out of the 1,385 shale-gas wells and near some conventional gas wells in the study area. For ten analytes for which uncensored data are abundant in this 3,000 km2 rural region, concentrations are unchanged or improved as compared to samples analyzed prior to 1990. Specifically, TDS, Fe, Mn, sulfate, and pH show small but statistically significant improvement, and As, Pb, Ba, Cl, and Na show no change. Evidence from this rural area could document improved groundwater quality caused by decreased acid rain (pH, sulfate) since the imposition of the Clean Air Act or decreased steel production (Fe, Mn). Such improvements have not been reported in groundwater in more developed areas of the U.S.