UNIVERSITY PARK, Pa. — Pumping carbon dioxide underground may help combat the warming of the atmosphere but finding appropriate underground sites that could safely serve as reservoirs can be complicated.
To address this complexity, a Penn State-led research team combined an artificial intelligence technique with an understanding of physics to develop an efficient, cost-effective predictive modeling approach. They published their results in the Journal of Contaminant Hydrology.
“Storing carbon dioxide underground is one environmentally friendly way to reduce the amount of the gas in the atmosphere,” said Parisa Shokouhi, associate professor of engineering science and mechanics. “But the geological structure can be unfavorable to carbon dioxide injection. For example, if pressure surpasses a certain limit, there can be fractures, gas leakage and earthquakes, and if you over-inject with too much gas, you can have similar issues.”
Numerical simulations, complex and detailed models used to help understand a problem that can’t be easily defined otherwise, have been used to predict a potential site’s response to carbon dioxide injection. These simulations, however, can be remarkably expensive and time-consuming to run. And for every new site being explored as a storage site candidate, a new numerical simulation must be run anew.
To avoid the cost and time commitment required with numerical simulation, the research team trained deep learning algorithms to make accurate predictions across a variety of scenarios. Learning from data produced by simulated scenarios of carbon dioxide in a 7,500-foot-deep reservoir, the algorithms were able to predict how carbon dioxide saturation and pressure would behave in new simulated systems.