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At the United Nations Climate Summit in Glasgow, the US pledged to reduce global methane emissions by 30 percent over the next decade. That’s a tall order and it has the oil and gas (O&G) industry scrambling to figure out how to cheaply detect and control its methane leakage–at the same time, it’s trying to justify and convince the public about the need to continue fracking.
Researchers at The University of New Mexico (UNM) might be able to help O&G reach climate change goals with a low-cost, portable sensor system for natural gas leakage monitoring and detection. The sensors are being developed at UNM’s Center for Micro-Engineered Materials, where they have worked on developing detection sensors for the past two years through a research project funded by grants from the Department of Energy (DOE). They recently received additional funding from the DOE to continue research and development, with the possibility of a third year if research advancement continues.
Research Associate Professor Lok-kun Tsui and Distinguished Professor Fernando Garzon, both from the Department of Chemical and Biological Engineering, lead the $1.5 million DOE-funded project. UNM has partnered with SensorComm Technologies to develop portable data acquisition and transmission technology needed for a sensor system.
“Our sensors are descended from automotive exhaust sensors we worked on a few years ago, so they can withstand harsh environments and exposure to sticky gases like ammonia. This low maintenance aspect makes them more suitable for long-term monitoring in the field,” Tsui explained.
Methane leaks cost billions per year and greatly contribute to global methane emissions. The O&G industry produces 30 percent of methane emissions in the US. New Mexico, the second-largest producing state behind Texas, has been enacting rules and regulations aimed at reining in emissions from O&G, particularly in the Permian Basin and in the northwest corner of the state, where a methane cloud roughly the size of Delaware can be seen from space.
It’s easier to detect leakage during the production of natural gas than to pinpoint exactly where the methane is coming from in the 300,000+ miles of pipeline transporting natural gas that crisscross the U.S. The leak must be identified as being either from a pipeline or from other sources of methane such as livestock or coastal wetlands. Such monitoring also gives early warnings of leaks so they can be repaired quickly.
“We have also trained machine-learning algorithms to both quantify methane concentrations and identify mixtures containing methane,” Tsui explained. “This allows us to distinguish methane emissions originating at natural gas infrastructure from sources such as wetlands and agriculture.”
The project’s next steps are to conduct more tests at low concentration limits, integrate the sensor system in a portable form and carry out a field test outside the laboratory.