A Hybrid GP-Fuzzy Approach for Reservoir
Characterization
T. Yu, D. Wilkinson, and D. Xie. Genetic Programming Theory and Practise, chapter 17, Kluwer, (2003)
Abstract
A hybrid GP-fuzzy approach to model reservoir
permeability is presented. This approach uses a
two-step divide-and-conquer process for modelling.
First, GP is applied to construct classifiers that
identify permeability ranges. Within each range, ANFIS
is employed to build a Takagi-Sugeno-Kang fuzzy
inference system that gives permeability estimation. We
applied this method to five well log data sets. The
results show that this hybrid system gives more
accurate permeability estimation than other previous
works.
%0 Book Section
%1 TinaYu:2003:GPTP
%A Yu, Tina
%A Wilkinson, Dave
%A Xie, Deyi
%B Genetic Programming Theory and Practise
%D 2003
%E Riolo, Rick L.
%E Worzel, Bill
%I Kluwer
%K Characterisation, Computing, Estimation, Exploration Fuzzy Logic, Modelling. Oil Permeability Production, Reservoir Soft algorithms, and genetic programming,
%P 271--290
%T A Hybrid GP-Fuzzy Approach for Reservoir
Characterization
%X A hybrid GP-fuzzy approach to model reservoir
permeability is presented. This approach uses a
two-step divide-and-conquer process for modelling.
First, GP is applied to construct classifiers that
identify permeability ranges. Within each range, ANFIS
is employed to build a Takagi-Sugeno-Kang fuzzy
inference system that gives permeability estimation. We
applied this method to five well log data sets. The
results show that this hybrid system gives more
accurate permeability estimation than other previous
works.
%& 17
%@ 1-4020-7581-2
@incollection{TinaYu:2003:GPTP,
abstract = {A hybrid GP-fuzzy approach to model reservoir
permeability is presented. This approach uses a
two-step divide-and-conquer process for modelling.
First, GP is applied to construct classifiers that
identify permeability ranges. Within each range, ANFIS
is employed to build a Takagi-Sugeno-Kang fuzzy
inference system that gives permeability estimation. We
applied this method to five well log data sets. The
results show that this hybrid system gives more
accurate permeability estimation than other previous
works.},
added-at = {2008-06-19T17:35:00.000+0200},
author = {Yu, Tina and Wilkinson, Dave and Xie, Deyi},
biburl = {https://www.bibsonomy.org/bibtex/27d55b5afbfaaf6b921a3c4eca54d6fa8/brazovayeye},
booktitle = {Genetic Programming Theory and Practise},
chapter = 17,
editor = {Riolo, Rick L. and Worzel, Bill},
interhash = {e50ddbb92a9af7e5b5e3fcc9316f5e1c},
intrahash = {7d55b5afbfaaf6b921a3c4eca54d6fa8},
isbn = {1-4020-7581-2},
keywords = {Characterisation, Computing, Estimation, Exploration Fuzzy Logic, Modelling. Oil Permeability Production, Reservoir Soft algorithms, and genetic programming,},
notes = {ChevronTexaco Information Technology Company and
ChevronTexaco Exploration and Production Technology
Company},
pages = {271--290},
publisher = {Kluwer},
size = {19 pages},
timestamp = {2008-06-19T17:55:03.000+0200},
title = {A Hybrid {GP}-Fuzzy Approach for Reservoir
Characterization},
year = 2003
}