Jiayang Wang, Selvaprabu Nadarajah, Jingfan Wang, Arvind P Ravikumar 

Reducing methane emissions from the oil and gas sector is a key component
of climate policy in the United States. Methane leaks across the supply chain
are stochastic and intermittent, with a small number of sites (‘super-emitters’)
responsible for a majority of emissions. Thus, cost-effective emissions reduction
critically relies on effectively identifying the super-emitters from thousands of well sites and millions of miles of pipelines. Conventional approaches such as walking
surveys using optical gas imaging technology are slow and time-consuming. In
addition, several variables contribute to the formation of leaks such as infrastructure
age, production, weather conditions, and maintenance practices. Here, we develop
a machine learning algorithm to predict high-emitting sites that can be prioritized
for follow-up repair. Such prioritization can significantly reduce the cost of surveys
and increase emissions reductions compared to conventional approaches. Our
results show that the algorithm using logistic regression performs the best out of
several algorithms. The model achieved a 70% accuracy rate with a 57% recall
and a 66% balanced accuracy rate. Compared to the conventional approach, the
machine learning model reduced the time to achieve a 50% emissions mitigation
target by 42%. Correspondingly, the mitigation cost reduced from $85/t CO2e to
$49/t CO2e.