AI in climate change: Machine learning helps predict methane well leaks

AI could have a key role to play in climate change after the technology was used by scientists to identify greenhouse gas leaks in oil and gas wells. Research conducted at the University of Vermont used machine learning algorithms to predict whether the wells would emit significant amounts of methane – one of the most harmful gases contributing to global warming. It tested 38,391 wells in Alberta, Canada, and was able to determine which wells leaked – and those that didn't – with up to 87% accuracy. Professor George Pinder, who conducted the research alongside former doctoral student James Montague, said: "The big picture is that we can now have tool that could help us much more efficiently identify leaking wells. "Given that methane is such a significant contributor to global warming, this is powerful information that should be put to use." The analysis yielded a cluster of 16 traits that predicted whether a well would fail and leak. Researchers were given access to more complete information, including the fluid properties of the oil or natural gas being mined, for 4,000 wells. For these wells, the machine learning algorithm identified leaks with 87% accuracy. For a larger sample of about 28,500 wells, where the fluid property was not known and taken into account, the accuracy level was 62%. Companies in Alberta are required to test wells at the time they begin operating to determine if they have failed and are leaking methane. They must also keep careful records of each well's construction characteristics. Professor Anthony R Ingraffea – based at Cornell University's School of Civil and Environmental Engineering, in Ithaca, New York – is an expert in oil and natural gas well design and construction, but was not involved in the study. He said: "Provincial and state regulatory agencies never have enough inspectors or financial resources to locate, let alone repair, leaking wells.