Their novel monitoring system can rapidly monitor carbon dioxide sequestered underground.
Capturing and storing carbon dioxide (CO2) deep underground can help combat climate change, but long-term monitoring of the stored CO2 within a geological storage site is difficult using current physics-based methods.
Texas A&M University researchers proved that unsupervised machine-learning methods could analyze the sensor-gathered data from a geological carbon-storage site and rapidly depict the underground CO2 plume locations and movements over time, lowering the risk of an unregistered CO2 escape.
Project lead Siddharth Misra, the Ted H. Smith, Jr. ’75 and Max R. Vordenbaum ’73 DVG Associate Professor in the Harold Vance Department of Petroleum Engineering, used seed money from the Texas A&M Energy Institute to begin the research.
“The project was designed to facilitate long-term CO2 storage at low risk,” said Misra. “Current physics-driven models are time consuming to produce and assume where the CO2 is in a storage site. We are letting the data tell us where the CO2 actually is. We are also providing rapid visualization because if you cannot see the CO2, you cannot control it deep underground.”
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