UNIVERSITY PARK, Pa. — Incorporating field data for the first time, researchers at Penn State demonstrated machine learning can be a powerful and cost-effective tool for monitoring sequestered carbon dioxide (CO2), overcoming a hurdle for the burgeoning technology aimed at combating climate change.
Carbon sequestration, already in use in at more than a dozen sites in the U.S., according to the Congressional Budget Office, could be a solution for more difficult areas of decarbonization such as transportation, because it stores greenhouse gases in a super compressed, liquid form below ground for hundreds of years. It’s critical, though, that the CO2 doesn’t disturb the aquifers and other areas above — or worse, reach the surface — as it would negate the carbon sink. These sites require millions of dollars in seismic monitoring throughout their lifespan and existing methods for tracking stored CO2 — and detecting possible leaks — take weeks or longer, the researchers said.
This new approach, detailed in the International Journal of Greenhouse Gas Control, shows machine learning can create efficient, cost-effective solutions to better monitor stored CO2 with a small amount of seismic data. These solutions can be processed within seconds, at a fraction of the cost of traditional seismic monitoring methods that need hundreds of seismic devices implanted thousands of feet below ground.
Zi Xian Leong, who earned his doctorate in geosciences from Penn State in 2023, developed the method with his then adviser, Tieyuan Zhu, associate professor of geosciences.
The U.S. Department of Energy (DOE) asked the researchers to investigate more cost-effective approaches to monitoring CO2 stored at the Frio-II site in Texas and provided the team with field data from the site. Yet, there was just one problem. Instead of utilizing many seismic sources — devices that emit seismic waves similar to sonar transducers to characterize inaccessible areas — as is typical in seismic surveys to assess the CO2 plume and surrounding rock, the researchers had access to only one source.
That prompted Leong, now employed at Chevron in Houston, Texas, to explore machine learning as a means to overcome the limitation posed by having only a single seismic source.
Leong said he knew that machine learning was useful for solving complex problems — including those related to geosciences. Researchers developed the approach by first training the machine learning algorithm by feeding in training dataset which encompasses the geology of its surrounding area, rock physics and fluid dynamics for liquefied CO2. From there, they computationally generated thousands of possible scenarios of how the stored gas could change and how those changes would trigger on a seismic record. This process took months to develop, but the effort resulted in the ability to monitor the stored CO2 in just seconds.