Journal
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
Volume 14, Issue 1, Pages 15-21Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/S0952-1976(00)00048-8
Keywords
neural networks; fuzzy logic; genetic algorithms; petroleum reservoirs; permeability; well logs
Ask authors/readers for more resources
This paper introduces a new neural-fuzzy technique combined with genetic algorithms in the prediction of permeability in petroleum reservoirs. The methodology involves the use of neural networks to generate membership functions and to approximate permeability automatically From digitized data (well logs) obtained from oil wells. The trained networks are used as fuzzy rules and hyper-surface membership Functions. The results of these rules are interpolated based on the membership grades and the parameters in the defuzzification operators which are optimized by genetic algorithms. The use of the integrated methodology is demonstrated via a case study in a petroleum reservoir in offshore Western Australia, The results show that the integrated neural-fuzzy-generic-algorithm (INFUGA) gives the smallest error on the unseen data when compared to similar algorithms. The INFUGA algorithm is expected to provide a significant improvement when the unseen data come from a mixed or complex distribution. (C) 2001 Elsevier Science Ltd. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available