A115-0003
Clearing the Air: Toward a Definition of Leak Detection and Quantification Methods

Friday, 11 December 2020
Poster
Daniel Zimmerle1, Clay Bell1, Kristine Bennett1, Chandler Kemp2 and Arvind P Ravikumar3, (1)Colorado State University, Energy Institute, Fort Collins, CO, United States, (2)Harrisburg University of Science and Technology, Harrisburg, PA, United States, (3)Harrisburg University, Harrisburg, PA, United States
Abstract:
Recent focus on methane emissions from fossil fuel operations has driven a rapid increase in interest in next-generation leak detection, quantification, and repair solutions to mitigate fugitive methane emissions. The diversity of solutions has driven demand for better characterization of their performance, including probability and time to detect leaks, understanding of lower detection limits and lock-out conditions for use, and how these solutions fit into larger leak detection and repair (LDAR) programs. While several controlled tests and field trials of solutions have been completed, the industry has yet to embrace, let alone standardize, testing methods for solutions. A new research program, Advancing Development of Emissions Detection (ADED), at Colorado State University’s Methane Emissions Technology Evaluation Center (METEC) in Fort Collins is developing test protocols that may be adopted as industry standards. The program is funded by the U.S. Department of Energy and industry partners, with strong participation by representatives from over 65 operators, leak detection solution developers, regulators, and environmental NGOs. Work on the project has highlighted the need to clearly categorize leak detection solutions. In this study, we recap the protocol development efforts in ADED, including: (a) the categorization of solutions required to develop common, robust, testing protocols that are clearly understood by all stakeholders; (b) the proposed structure for controlled testing and field trials; (c) reporting methods and metrics for the key types of leak detection solutions. Results of this study are of broad interest, as next-generation data streams will soon provide rich data streams which were previously not available to the research community.