Methane Leaks from Natural Gas Systems Follow Extreme Distributions

Future energy systems
may rely on natural gas as a low-cost fuel
to support variable renewable power. However, leaking natural gas
causes climate damage because methane (CH4) has a high
global warming potential. In this study, we use extreme-value theory
to explore the distribution of natural gas leak sizes. By analyzing
∼15 000 measurements from 18 prior studies, we show
that all available natural gas leakage data sets are statistically
heavy-tailed, and that gas leaks are more extremely distributed than
other natural and social phenomena. A unifying result is that the
largest 5% of leaks typically contribute over 50% of the total leakage
volume. While prior studies used log-normal model distributions, we
show that log-normal functions poorly represent tail behavior. Our
results suggest that published uncertainty ranges of CH4 emissions are too narrow, and that larger sample sizes are required
in future studies to achieve targeted confidence intervals. Additionally,
we find that cross-study aggregation of data sets to increase sample
size is not recommended due to apparent deviation between sampled
populations. Understanding the nature of leak distributions can improve
emission estimates, better illustrate their uncertainty, allow prioritization
of source categories, and improve sampling design. Also, these data
can be used for more effective design of leak detection technologies.