No abstract

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.

Conflicting evidence exists as to whether or not unconventional oil and gas (UOG) development has enhanced methane transport into groundwater aquifers over the past 15 years. In this study, recent groundwater samples were collected from 90 domestic wells and 4 springs in Northeastern Pennsylvania located above the Marcellus Shale after more than a decade of UOG development. No statistically significant correlations were observed between the groundwater methane level and various UOG geospatial metrics, including proximity to UOG wells and well violations, as well as the number of UOG wells and violations within particular radii. The δ 13 C and methane-tohigher chain hydrocarbon signatures suggested that the elevated methane levels were not attributable to UOG development nor could they be explained by using simple biogenic−thermogenic end-member mixing models. Instead, groundwater methane levels were significantly correlated with geochemical water type and topographical location. Comparing a subset of contemporary methane measurements to their co-located pre-drilling records (n = 64 at 49 distinct locations) did not indicate systematic increases in methane concentration but did reveal several cases of elevated concentration (n = 12) across a spectrum of topographies. Multiple lines of evidence suggested that the high-concentration groundwater methane could have originated from shallow thermogenic methane that migrated upward into groundwater aquifers with Appalachian Basin brine.

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 offers a robust and scalable framework for assessing groundwater contamination risks.

Hydrogeologic transport contributes to limited organic chemical contamination in a region of intense gas extraction, even 10 years post-development.