首页|Findings from University of Texas Austin Yields New Findings on Machine Learning (Development of a Hydrate Risk Assessment Tool Based On Machine Learning for Co2 Storage In Depleted Gas Reser- voirs)
Findings from University of Texas Austin Yields New Findings on Machine Learning (Development of a Hydrate Risk Assessment Tool Based On Machine Learning for Co2 Storage In Depleted Gas Reser- voirs)
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2024 FEB 02 (NewsRx) – By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on Machine Learning have been published. According to news reporting out of Austin, Texas, by NewsRx editors, research stated, “Depleted gas reservoirs are attractive sites for Carbon Capture and Storage (CCS) due to their huge storage capacities, proven seal integrity, existing infrastructure and subsurface data availability. However, CO2 injection into depleted formations can potentially lead to hydrate formation near the wellbore due to Joule-Thomson cooling, which might cause injectivity issues.” Financial supporters for this research include Center for Subsurface Energy and the Environment at The University of Texas at Austin, ITOCHU Oil Exploration Co., Ltd..
AustinTexasUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine LearningUniversity of Texas Austin
NETL
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Elsevier
