Forecasting PVT properties of crude oil systems based on support vector machines modeling scheme

Journal of Petroleum Science and Engineering

Volumn 64, Issue 1-4, 2009, Pages 25-34

  El Sebakhy, Emad A ^a  

^a ELSEVIER MASSON   (France)

Author keywords

bubble point pressure; empirical correlations; feedforward neural networks; Formation Volume Factor; PVT properties; Support Vector Machines Regression

Indexed keywords

BUBBLE POINT PRESSURE; EMPIRICAL CORRELATIONS; FORMATION VOLUME FACTOR; PVT PROPERTIES; SUPPORT VECTOR MACHINES REGRESSION;

ARTIFICIAL INTELLIGENCE; BOTTOM HOLE PRESSURE; CRUDE PETROLEUM; FEEDFORWARD NEURAL NETWORKS; GAS INDUSTRY; IMAGE RETRIEVAL; INFORMATION MANAGEMENT; LIQUEFIED PETROLEUM GAS; NANOFLUIDICS; PETROLEUM INDUSTRY; PETROLEUM REFINERIES; PETROLEUM RESERVOIR ENGINEERING; REGRESSION ANALYSIS; SUPPORT VECTOR MACHINES; VECTORS;

NEURAL NETWORKS;

ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; CORRELATION; CRUDE OIL; DECISION SUPPORT SYSTEM; FORECASTING METHOD; HYDROCARBON RESERVOIR; NONLINEARITY; NUMERICAL MODEL; OIL INDUSTRY; PERFORMANCE ASSESSMENT; PERMEABILITY; RESERVOIR CHARACTERIZATION; VECTOR AUTOREGRESSION;

EID: 59349086120     PISSN: 09204105     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.petrol.2008.12.006     Document Type: Article

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