Support vector regression for porosity prediction in a heterogeneous reservoir: A comparative study

Computers and Geosciences

Volumn 36, Issue 12, 2010, Pages 1494-1503

  Al Anazi, A F ^a   Gates, I D ^a  

^a UNIVERSITY OF CALGARY   (Canada)

Author keywords

Core data; Log data; Neural networks; Porosity estimation; Support vector machines

Indexed keywords

CLASSIFICATION TASKS; COMPARATIVE STUDIES; CONTROL MODEL; CORE DATA; EMPIRICAL RISK MINIMIZATION; EMPIRICAL RISKS; ESTIMATION PROBLEM; EXPECTED RISK; GENERAL REGRESSION NEURAL NETWORK; GENERALIZATION ABILITY; GLOBAL CORRELATION; HETEROGENEOUS FORMATION; HETEROGENEOUS RESERVOIRS; HIGHER-DIMENSIONAL; INPUT SPACE; INTELLIGENT TECHNIQUES; KERNEL FUNCTION; LEARNING METHODS; LOCAL CORRELATIONS; LOG DATA; LOSS FUNCTIONS; MODEL COMPLEXITY; MULTI LAYER PERCEPTRON; NEURAL NETWORK METHOD; NONLINEAR PROBLEMS; OFFSET WELLS; OPTIMAL SOLUTIONS; OPTIMIZATION METHOD; POROSITY ESTIMATION; POROSITY PREDICTIONS; RADIAL BASIS FUNCTION NEURAL NETWORKS; SAMPLE DATA; SANDSTONE RESERVOIRS; STATISTICAL ESTIMATION; STATISTICAL LEARNING THEORY; STRUCTURAL RISK MINIMIZATION PRINCIPLE; SUB-REGIONS; SUPPORT VECTOR; SUPPORT VECTOR REGRESSIONS; TRAINING DATA; UPPER BOUND; WELL LOG DATA;

ESTIMATION; FORECASTING; MATHEMATICAL MODELS; OPTIMAL SYSTEMS; OPTIMIZATION; POROSITY; RADIAL BASIS FUNCTION NETWORKS; REGRESSION ANALYSIS; RISK PERCEPTION; STATISTICS; SUPPORT VECTOR MACHINES; VECTORS; WELL LOGGING;

MULTILAYER NEURAL NETWORKS;

ACCURACY ASSESSMENT; ALGORITHM; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; COMPARATIVE STUDY; CORE LOGGING; CORRELATION; ESTIMATION METHOD; NUMERICAL MODEL; POROSITY; PREDICTION; REGRESSION ANALYSIS; RESERVOIR; WELL LOGGING;

EID: 77952428938     PISSN: 00983004     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cageo.2010.03.022     Document Type: Article

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