Journal
COMPUTERS & GEOSCIENCES
Volume 35, Issue 12, Pages 2314-2330Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.cageo.2009.04.010
Keywords
Committee fuzzy inference system; Sugeno; Larsen; Mamdani; Hybrid genetic algorithm-pattern search; Probabilistic neural network; Petrophysical data; Seismic attributes
Funding
- National Iranian Oil Company (NIOC)
- Vice-President of Research and Technology of the University of Tehran [6105023/1/02]
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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. (C) 2009 Elsevier Ltd. All rights reserved.
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