Petrophysical data prediction from seismic attributes using committee fuzzy inference system

This study presents an intelligent model based on fuzzy systems for making a quantitative formulation between seismic attributes and petrophysical data. The proposed methodology comprises two major steps. Firstly, the petrophysical data, including water saturation ( S w ) and porosity, are predicted...

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Vydáno v:	Computers & geosciences Ročník 35; číslo 12; s. 2314 - 2330
Hlavní autoři:	Kadkhodaie-Ilkhchi, Ali, Rezaee, M. Reza, Rahimpour-Bonab, Hossain, Chehrazi, Ali
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Kidlington Elsevier Ltd 01.12.2009 Elsevier
Témata:	Committee fuzzy inference system Earth sciences Earth, ocean, space Earthquake construction Exact sciences and technology Fuzzy Fuzzy logic Fuzzy set theory Hybrid genetic algorithm-pattern search Hydrocarbons Inference Larsen Mamdani Mathematical models Petrophysical data Probabilistic neural network Sedimentary rocks Seismic attributes Seismic engineering Seismic phenomena Stratigraphy Sugeno Middle East Persian Gulf Indian Ocean Iran Asia ISW, Arabian Sea, Persian Gulf Petrophysical data Committee fuzzy inference system Sugeno Probabilistic neural network Larsen Mamdani Seismic attributes Hybrid genetic algorithm-pattern search reservoir rocks algorithms neural networks reservoirs seismic profiles sandstone saturation synthetic seismograms correlation coefficient performances computers clastic rocks models maps hydrocarbons porosity fuzzy logic data processing offshore sedimentary rocks oil and gas fields prediction three-dimensional seismics petroleum exploration
ISSN:	0098-3004, 1873-7803
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