Detecting anomalous methane in groundwater within hydrocarbon production areas across the United States
Autor: | Mengqi Liu, Guanjie Zheng, Susan L. Brantley, Josh Woda, Tao Wen |
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Rok vydání: | 2021 |
Předmět: |
Environmental Engineering
Colorado 0208 environmental biotechnology Alkalinity New York Soil science Aquifer 02 engineering and technology 010501 environmental sciences Natural Gas 01 natural sciences Methane chemistry.chemical_compound Brining Oil and Gas Fields Sulfate Turbidity Waste Management and Disposal Groundwater 0105 earth and related environmental sciences Water Science and Technology Civil and Structural Engineering geography geography.geographical_feature_category Ecological Modeling Pennsylvania Pollution Texas Hydrocarbons 020801 environmental engineering Salinity chemistry Environmental science Water Pollutants Chemical Environmental Monitoring |
Zdroj: | Water research. 200 |
ISSN: | 1879-2448 |
Popis: | 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. |
Databáze: | OpenAIRE |
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