GSI Environmental Inc. (GSI), with project partners Utah State University (USU), Colorado State University (CSU), and Houston Advanced Research Center (HARC), have employed a robust field measurement methodology to detect and quantify annual methane emissions from natural gas storage wells at depleted reservoir and salt dome storage facilities. Multiple sampling events have been performed to measure methane emissions from i) above-ground equipment leaks, and ii) seepage at the ground surface from underground leaks near the wellbore over the full range of expected field conditions. Seasonal variability in climate and operations is being examined to better understand the true emissions from gas storage operations. A novel in-ground network of sensors has been installed to monitor the rates of methane loss due to below-ground seepage near the well bore.
GSI Environmental Inc., Austin, Texas 78759
Natural gas storage is necessary to ensure that U.S. natural gas supplies are continuously available, especially at times and locations where highly fluctuating peaks in demand exceed relatively constant levels of gas production. Knowledge of methane emissions from gas storage wells is currently limited and could constitute millions of dollars of natural gas lost to the atmosphere every year.
Currently, methane emissions are not accurately represented in EPA’s Green House Gas Inventory (GHGI). Current estimates for emissions from underground natural gas storage wells are based on out-of-date data from gas production wells. In addition, current estimates account only for surface infrastructure components (pipe connections, valves, etc.) and do not consider potential underground leakage. Therefore, storage wellhead sources need to be investigated to quantify methane emissions and reduce product loss.
Improved efficiencies will be realized by having deployed a combination of state-of-the-art technologies and quantitative analyses to reduce uncertainty associated with methane emissions. The technical team working on this research has an intimate understanding of the nature of the challenges posed by air emissions impacts, and is keenly aware of the relationship between economic development and protection of natural resources. Therefore, to reduce emissions from the oil and gas sector, we must better understand the sources of emissions and how best to measure emissions from those sources. This project seeks to utilize existing technologies in an innovative approach to i) establish time series and leak frequency at natural gas storage wells and fields, and ii) develop a long-term sensing system for monitoring real-time methane emissions. Such data and tools are critical for reducing both environmental and economic impacts of methane emissions from gas storage fields. This research will have a significant impact on the national estimates of methane emissions from the natural gas industry and will support operators’ targeted leak repair programs for reducing product loss from their operations.
The ongoing monitoring of a network of in-ground sensors at wells with below-ground seepage near the well bore will allow the true variability of seepage emissions to be continuously measured over the next year. This cannot be accomplished by discrete measurements. Additional data collection and analysis efforts will be used to refine, calibrate, verify, and validate algorithms capable of reliably estimating ground-based methane seepage based on easily, inexpensively measurable or estimable site-specific parameters and less intensive soil monitoring efforts at other sites in the future, thus providing an early leak-detection system for operators to reduce the potential for catastrophic events and minimize product loss.
Emission factor development is underway for above-ground infrastructure components at depleted reservoir and salt cavern wells.
Final Report [PDF] - January 2020
Project Continuation Report [PDF] - August 2018
Quarterly Research Performance Progress Report [PDF] July - September, 2018
Quarterly Research Performance Progress Report [PDF] April - June, 2018
Quarterly Research Performance Progress Report [PDF] October - December, 2017
Quarterly Research Performance Progress Report [PDF] July - September, 2017
Quarterly Research Performance Progress Report [PDF] April - June, 2017
Quarterly Research Performance Progress Report [PDF] January - March, 2017
Quarterly Research Performance Progress Report [PDF] October - December, 2016