Physics-informed deep learning to assess carbon dioxide storage sites

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."