Petrophysical data prediction from seismic attributes using committee fuzzy inference system |
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| Authors: | Ali Kadkhodaie-Ilkhchi M Reza Rezaee Hossain Rahimpour-Bonab Ali Chehrazi |
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| Affiliation: | aDepartment of Geology, College of Science, University of Tehran, Tehran, Iran;bDepartment of Petroleum Engineering, Curtin University of Technology, ARRC Building, 26 Dick Perry Avenue, Kensington, Perth, WA 6151, Australia;cGeology Division, Iranian Offshore Oilfields Company, No. 38, Tooraj St., Vali-Asr Ave., NIOC, Tehran 19395, Iran |
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| Abstract: | 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 (Sw) 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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| Keywords: | Committee fuzzy inference system Sugeno Larsen Mamdani Hybrid genetic algorithm-pattern search Probabilistic neural network Petrophysical data Seismic attributes |
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