A new void fraction correlation inferred from artificial neural networks for modeling two-phase flows in geothermal wells

Computers and Geosciences

Volumn 41, Issue , 2012, Pages 25-39

  Alvarez del Castillo A ^a   Santoyo, E ^a   Garcia Valladares O ^a  

^a INSTITUTO DE ENERGÍAS RENOVABLES   (Mexico)

Author keywords

Artificial intelligence; Geothermal energy; Liquid holdup; Pressure gradients; Simulation; Statistics

Indexed keywords

ARTIFICIAL NETWORKS; DIMENSIONLESS NUMBER; FLUID DENSITIES; HIDDEN LAYERS; HYPERBOLIC TANGENT; INPUT PARAMETER; LEVENBERG-MARQUARDT ALGORITHM; LINEAR ACTIVATION FUNCTION; LIQUID HOLDUP; MEASURED FIELD; NEW CORRELATIONS; NUMERICAL MODELING; PRODUCTION DATA; REYNOLDS; SIMULATION; STATISTICAL COMPARISONS; STATISTICAL EVALUATION; STATISTICAL PARAMETERS; STEAM QUALITY; WELLBORE; WELLHEAD PRESSURES;

ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; GEOTHERMAL ENERGY; GEOTHERMAL WELLS; NEURAL NETWORKS; OIL FIELD EQUIPMENT; PRESSURE GRADIENT; STATISTICS; VOID FRACTION;

TWO PHASE FLOW;

ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; BACK PROPAGATION; COMPUTER SIMULATION; CORRELATION; FLOW MODELING; FROUDE NUMBER; GEOTHERMAL ENERGY; NUMERICAL MODEL; PRESSURE GRADIENT; REYNOLDS NUMBER; TWO PHASE FLOW; VOID RATIO; WELLHEAD;

EID: 84857915080     PISSN: 00983004     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cageo.2011.08.001     Document Type: Article

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