Characterizing detection probabilities of advanced mobile leak surveys: Implications for sampling effort and leak size estimation in natural gas distribution systems

Abstract

Advanced leak detection (ALD) to survey local natural gas distribution systems has reached a point in technological maturity where new federal regulations will require its use in compliance surveys. Because most of these deployments are conducted by commercial providers, there has been little publicly available data documenting characteristics of the underlying methane (CH4) plumes that are the core features measured in ALD surveys. Here, we document key features of CH4 plumes measured in ALD surveys of 15 U.S. metropolitan areas where we had deployed high-sensitivity CH4 analyzers on Google Street View cars. Our analysis reveals that CH4 concentration enhancements from CH4 sources exhibit high temporal variability, often differing by more than 10-fold among repeated observations. This variability introduces challenges for estimating source emission rates because the same source can appear to be large on one drive-by and small on the next. Additionally, the frequency distribution of CH4 enhancements from a given source generally has a strong positive skew that can lead to overestimation of leak size. The magnitude of CH4 enhancements from a source measured with a mobile sensor can also change quickly over time, as indicated by decreasing temporal correlation between mobile measurements longer than an approximately hourly time scale. To manage the uncertainty, we demonstrate how additional survey effort can help overcome this variability and instability to allow discrimination among the wide range of leak sizes. We quantify the probability of source detection, finding that it increases with estimated leak size. Combining these results, we develop a simulation that demonstrates the potential for ALD to detect leaks and quantify emissions as a function of sampling (driving) effort. Our results suggest that five to eight drives of each roadway in a target area would detect >90% of leaks and provide adequate emissions quantification for repair/replacement prioritization.