Researchers Monitor Subsurface CO2 Storage

Texas A&M University researchers demonstrated that unsupervised machine learning algorithms could analyze data from a geological carbon sequestration site to determine and model underground carbon dioxide (CO₂) plume locations and movements.

The algorithms evaluate CO₂ presence in the data using five broad or qualitative ranges, identified by color for a two-dimensional visual representation; their results accelerated the pinpointing of plume location, coverage area, and its approximate size, shape, and density.

Texas A&M's Siddarth Misra said, "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."

From Texas A&M Today
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