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Horizontal drilling with hydraulic fracturing (HDHF) relies on the use of anthropogenic organic chemicals in proximity to residential areas, raising concern for groundwater contamination. Here, we extensively characterized organic contaminants in 94 domestic groundwater sites in Northeastern Pennsylvania after ten years of activity in the region. All analyzed volatile and semi-volatile compounds were below recommended United States Environmental Protection Agency maximum contaminant levels, and integrated concentrations across two volatility ranges, gasoline range organic compounds (GRO) and diesel range organic compounds (DRO), were low (0.13 ± 0.06 to 2.2 ± 0.7 ppb and 5.2–101.6 ppb, respectively). Following dozens of correlation analyses with distance-to-well metrics and inter-chemical indicator correlations, no statistically significant correlations were found except: (1) GRO levels were higher within 2 km of violations and (2) correlation between DRO and a few inorganic species (e.g., Ba and Sr) and methane. The correlation of DRO with inorganic species suggests a potential high salinity source, whereas elevated GRO may result from nearby safety violations. Highest-concentration DRO samples contained bis-2-ethylhexyl phthalate and N,N-dimethyltetradecylamine. Nevertheless, the overall low rate of contamination for the analytes could be explained by a spatially-resolved hydrogeologic model, where estimated transport distances from gas wells over the relevant timeframes were short relative to the distance to the nearest groundwater wells. Together, the observations and modeled results suggest a low probability of systematic groundwater organic contamination in the region.

© 2020. American Geophysical Union. All Rights Reserved. The rapid expansion of unconventional oil and gas development (UD), made possible by horizontal drilling and hydraulic fracturing, has triggered concerns over groundwater contamination and public health risks. To improve our understanding of the risks posed by UD, we develop a physically based, spatially explicit framework for evaluating groundwater well vulnerability to aqueous phase contaminants released from surface spills and leaks at UD well pad locations. The proposed framework utilizes the concept of capture probability and incorporates decision-relevant planning horizons and acceptable risks to support goal-oriented modeling for groundwater protection. We illustrate the approach in northeastern Pennsylvania, where a high intensity of UD activity overlaps with local dependence on domestic groundwater wells. Using two alternative models of the bedrock aquifer and a precautionary paradigm to integrate their results, we found that most domestic wells in the domain had low vulnerability as the extent of their modeled probabilistic capture zones were smaller than distances to the nearest existing UD well pad. We also found that simulated capture probability and vulnerability were most sensitive to the model parameters of matrix hydraulic conductivity, porosity, pumping rate, and the ratio of fracture to matrix conductivity. Our analysis demonstrated the potential inadequacy of current state-mandated setback distances that allow UD within the boundaries of delineated capture zones. The proposed framework, while limited to aqueous phase contamination, emphasizes the need to incorporate information on flow paths and transport timescales into policies aiming to protect groundwater from contamination by UD.

Contamination from anthropogenic activities is a long-standing challenge to the sustainability of groundwater resources. Physically based (PB) models are often used in groundwater risk assessments, but their application to large scale problems requiring high spatial resolution remains computationally intractable. Machine learning (ML) models have emerged as an alternative to PB models in the era of big data, but the necessary number of observations may be impractical to obtain when events are rare, such as episodic groundwater contamination incidents. The current study employs metamodeling, a hybrid approach that combines the strengths of PB and ML models while addressing their respective limitations, to evaluate groundwater well vulnerability to contamination from unconventional oil and gas development (UD). We illustrate the approach in northeastern Pennsylvania, where intensive natural gas production from the Marcellus Shale overlaps with local community dependence on shallow aquifers. Metamodels were trained to classify vulnerability from predictors readily computable in a geographic information system. The trained metamodels exhibited high accuracy (average out-of-bag classification error <5%). A predictor combining information on topography, hydrology, and proximity to contaminant sources (inverse distance to nearest upgradient UD source) was found to be highly important for accurate metamodel predictions. Alongside violation reports and historical groundwater quality records, the predicted vulnerability provided critical insights for establishing the prevalence of UD contamination in 94 household wells that we sampled in 2018. While <10% of the sampled wells exhibited chemical signatures consistent with UD produced wastewaters, >60% were predicted to be in vulnerable locations, suggesting that future impacts are likely to occur with greater frequency if safeguards against contaminant releases are relaxed. Our results show that hybrid physics-informed ML offer