Researchers at University of Melbourne Target Machine Learning (Graph Theory Bas ed Estimation of Probable Co2 Plume Spreading In Siliciclastic Reservoirs With L ithological Heterogeneity)
Researchers at University of Melbourne Target Machine Learning (Graph Theory Bas ed Estimation of Probable Co2 Plume Spreading In Siliciclastic Reservoirs With L ithological Heterogeneity)
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Current study results on Machine Learning have be en published. According to news reporting originating from Melbourne, Australia, by NewsRx correspondents, research stated, “Estimating plume spreading in geolo gical CO2 storage reservoirs is critical for several reasons including the asses sment of pore space utilization efficiency, preferential CO2 migration pathways and trapping. However, plume spreading critically depends on lithological hetero geneity of the reservoir and CO2 injection rate.” Financial support for this research came from Southeast Regional Carbon Utilizat ion and Storage Acceleration (SECARB-USA) Initiative under the U.S. Department o f Energy.
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Melbourne/Australia/Australia and New Zealand/Cyborgs/Emerging Technologies/Machine Learning/Mathematical Theories/Mathematics/Numerical Modeling/University of Melbourne
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