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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Bibliographic Details
Published in:Computers & geosciences Vol. 35; no. 12; pp. 2314 - 2330
Main Authors: Kadkhodaie-Ilkhchi, Ali, Rezaee, M. Reza, Rahimpour-Bonab, Hossain, Chehrazi, Ali
Format: Journal Article
Language:English
Published: Kidlington Elsevier Ltd 01.12.2009
Elsevier
Subjects:
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
Online Access:Get full text
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Summary: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 from seismic attributes using various fuzzy inference systems (FISs), including Sugeno (SFIS), Mamdani (MFIS) and Larsen (LFIS). Secondly, a committee fuzzy inference system (CFIS) is constructed using a hybrid genetic algorithms-pattern search (GA-PS) technique. The inputs of the CFIS model are the outputs and averages of the FIS petrophysical data. The methodology is illustrated using 3D seismic and petrophysical data of 11 wells of an Iranian offshore oil field in the Persian Gulf. The performance of the CFIS model is compared with a probabilistic neural network (PNN). The results show that the CFIS method performed better than neural network, the best individual fuzzy model and a simple averaging method.
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ISSN:0098-3004
1873-7803
DOI:10.1016/j.cageo.2009.04.010