Date of Award
Summer 7-1-2022
Degree Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Earth Sciences
Advisor(s)
Lautz, Laura K.
Keywords
carbon footprint, groundwater, machine learning, methane, time-series
Subject Categories
Earth Sciences | Geochemistry | Hydrology | Physical Sciences and Mathematics
Abstract
The Marcellus Shale is the largest shale gas play in the U.S. production of natural gas using high-volume hydraulic fracturing (HVHF) and production is prevalent throughout the play except in New York (NY), where it is currently banned. High concentrations of methane, the main component of natural gas, in groundwater, as well as its presence in the atmosphere, can have negative consequences. In this dissertation, three aspects of this issue are explored: 1) how and why naturally-occurring methane concentrations vary through time; 2) how elevated naturally-occurring methane concentrations in domestic water wells can be predicted from commonly observed well characteristics; and 3) how pumping of domestic groundwater wells can contribute to direct methane emission to the atmosphere. Regulatory agencies routinely assess the presence of stray gas release from unconventional HVHF gas wells by sampling for methane in nearby groundwater after the well is drilled or if citizens complain about methane in their water. We studied whether methane concentrations in groundwater naturally vary through time in NY to test the assumption that pre-drilling observations of well water quality can be reliable measures for assessing impacts of later gas drilling. We collected groundwater samples from 11 domestic wells in New York monthly for 13 months for methane and ion concentrations in a highly gas productive part of the Appalachian basin where HVHF has been banned. Changing methane concentrations correlated with changes in chloride and bromide, indicating changing mixtures of shallow freshwater and deeper formation brine extracted by the wells through time. The hydrogeologic setting of a water well can cause variability in methane concentrations that may mimic contamination by stray gas but cannot be attributed to gas drilling. For this reason, before and after testing has limited utility to distinguish impacts of gas drilling from other causes of changing methane concentrations unless that testing includes sampling a comprehensive set of ions measured multiple times prior to drilling. In addition to groundwater methane concentrations changing through time, they can also vary spatially as a function of the hydrogeologic setting. High dissolved methane concentrations in groundwater in shale gas areas can be indicative of either baseline conditions or introduction of stray gas from nearby development of natural gas wells. Data-driven models that predict locations with naturally high methane concentrations could inform assessment of attribution in cases where the source of the elevated methane is not known. We trained decision tree models using well characteristics and water quality data for 360 domestic wells over the Marcellus Shale in NY to predict which well have naturally high baseline methane concentrations in groundwater. We assessed the performance of decision tree models that use different thresholds for defining high methane concentrations, finding similar results amongst all but the highest threshold. We then evaluated model performance when differing types of information were used to train the decision tree. Our results show that hydrochemical parameters are good predictors of high methane in groundwater, but geospatial parameters are not. Sulfate concentration and specific conductance measurements can be used to effectively predict whether wells have ≥2 mg/L methane. Further, sulfate observations alone can be used to predict groundwater wells with ≥10 mg/L methane. Data-driven decision tree models trained on observational data can be used as a simple screening tool to identify domestic groundwater wells that are likely to contain high baseline methane concentrations. Despite the abundance of methane in groundwater, gas emissions from home water use are not included in greenhouse gas (GHG) inventories. We used dissolved methane observations from 11,743 domestic water wells to estimate direct emissions from groundwater pumping over a portion of the Marcellus Shale. We found that methane from groundwater is negligible in regional GHG inventories but can comprise a sizeable portion of an individual household carbon footprint. Our work indicates that focused mitigation of methane emissions from homes with the highest methane concentrations would have an outsized impact because 5% of domestic wells are responsible for 80% of emissions from groundwater pumping in the Marcellus play.
Access
Open Access
Recommended Citation
Campbell, Amanda E., "Patterns of Dissolved Methane in Groundwater and Its Contribution to Emissions Inventories" (2022). Dissertations - ALL. 1545.
https://surface.syr.edu/etd/1545
