首页|Research Data from Virginia Polytechnic Institute and State University (Virginia Tech) Update Understanding of Machine Learning (Transport Properties of Oil-co2 Mixtures In Calcite Nanopores: Physics and Machine Learning Models)
Research Data from Virginia Polytechnic Institute and State University (Virginia Tech) Update Understanding of Machine Learning (Transport Properties of Oil-co2 Mixtures In Calcite Nanopores: Physics and Machine Learning Models)
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NETL
NSTL
Elsevier
Investigators discuss new findings in Machine Learning. According to news reporting out of Blacksburg, Virginia, by NewsRx editors, research stated, “Fundamental understanding and quantitative models of the transport properties of oil-CO2 mixtures in nanopores are indispensable for physics-based models of CO2-enhanced oil recovery in unconventional oil reservoirs. This study determines the Maxwell- Stefan (M-S) diffusivities of CO2-decane (1: CO2; 2: decane /C10) mixtures in calcite nanopores with compositions relevant to CO2 Huff-n-Puff by molecular dynamics (MD) simulations.”
BlacksburgVirginiaUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine LearningVirginia Polytechnic Institute and State University (Virginia Tech)
NETL
NSTL
Elsevier
