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.