Support vector regression to predict porosity and permeability: Effect of sample size

Computers and Geosciences

Volumn 39, Issue , 2012, Pages 64-76

  Al Anazi, A F ^a   Gates, I D ^a  

^a UNIVERSITY OF CALGARY   (Canada)

Author keywords

Artificial neural networks; Core data; Log data; Loss function; Permeability prediction; Porosity prediction; Small sample size; Support vector machines

Indexed keywords

CORE DATA; LOG DATA; LOSS FUNCTION; PERMEABILITY PREDICTION; POROSITY PREDICTION; SMALL SAMPLE SIZE; SUPPORT VECTOR;

CORE ANALYSIS; FORECASTING; GAS INDUSTRY; GEOPHYSICS; OIL FIELDS; OIL WELLS; PETROLEUM RESERVOIR ENGINEERING; POROSITY; REGRESSION ANALYSIS; RISK PERCEPTION; SAMPLING; WELLS;

NEURAL NETWORKS;

ARTIFICIAL NEURAL NETWORK; CORE ANALYSIS; CORRELATION; GAS FIELD; HYDROCARBON EXPLORATION; LABORATORY METHOD; OIL FIELD; PARAMETERIZATION; PERMEABILITY; POROSITY; PREDICTION; SANDSTONE; VECTOR AUTOREGRESSION;

EID: 84855555199     PISSN: 00983004     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cageo.2011.06.011     Document Type: Article

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