CFP last date
20 December 2024
Reseach Article

Design of Fuzzy Subtractive Clustering Model using Particle Swarm Optimization for the Permeability Prediction of the Reservoir

by Shahram Mollaiy Berneti
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 29 - Number 11
Year of Publication: 2011
Authors: Shahram Mollaiy Berneti
10.5120/3687-5117

Shahram Mollaiy Berneti . Design of Fuzzy Subtractive Clustering Model using Particle Swarm Optimization for the Permeability Prediction of the Reservoir. International Journal of Computer Applications. 29, 11 ( September 2011), 33-37. DOI=10.5120/3687-5117

@article{ 10.5120/3687-5117,
author = { Shahram Mollaiy Berneti },
title = { Design of Fuzzy Subtractive Clustering Model using Particle Swarm Optimization for the Permeability Prediction of the Reservoir },
journal = { International Journal of Computer Applications },
issue_date = { September 2011 },
volume = { 29 },
number = { 11 },
month = { September },
year = { 2011 },
issn = { 0975-8887 },
pages = { 33-37 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume29/number11/3687-5117/ },
doi = { 10.5120/3687-5117 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:15:34.070106+05:30
%A Shahram Mollaiy Berneti
%T Design of Fuzzy Subtractive Clustering Model using Particle Swarm Optimization for the Permeability Prediction of the Reservoir
%J International Journal of Computer Applications
%@ 0975-8887
%V 29
%N 11
%P 33-37
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Permeability is the key parameter of the reservoir and has a significant impact on petroleum fields operations and reservoir management. In most reservoirs, permeability measurements are rare and therefore permeability must be measured in the laboratory from reservoir core samples or evaluated from well test data. However, core analysis and well test data are usually only available from a few wells in a field. Unfortunately, coring every well in large fields is very expensive and uneconomical. This paper proposes an intelligent technique using a Takagi-Sugeno-Kang (TSK) fuzzy modeling approach based on subtractive clustering and particle swarm optimization (PSO) to predict reservoir permeability from well logs data. Subtractive clustering technique (SCT) is employed to identify fuzzy inference system. The radius of influence of cluster center ( ) in the SCT is selected by PSO. This intelligent technique is applied to predict permeability of Mansuri Bangestan reservoir located in Ahwaz, Iran utilizing available geophysical well log data. The performance of the technique is recorded in terms of MSE and value. The results showed that the proposed technique was well performed in predicting the reservoir permeability.

References
  1. Zadeh, L.A., 1973. Outline of a newapproach to the analysis of complex systems and decision processes. IEEE Transaction on Systems,Man, and Cybernetics 3, 28–44.
  2. Tagaki, T., Sugeno, M., 1985. Fuzzy identification of systems and its application to modeling and control. IEEE Transactions on Systems, Men and Cybernetics 15, 116–132.
  3. Sugeno, M., Kang, G.T., 1988. Structure identification of fuzzy model. Fuzzy Sets and Systems 28, 15–33.
  4. Chiu SL., 1994. Fuzzy model identification based on cluster estimation. J Intell Fuzzy Syst; 2:267–78.
  5. Yager R, Filev D., 1994. Generation of fuzzy rules by mountain method. J Intell Fuzzy Syst; 2:209–19.
  6. Goldberg, D., 1989. Genetic algorithms in search, optimization and machine learning. Reading, MA: Addison-Wesley (pp. 1-25).
  7. Kirkpatrick, S., Gelatt, C. D., and Vecchi, M. P., 1983. “Optimization by simulated annealing,” Science, vol. 220, pp. 671–680.
  8. Dorigo, M., Maniezzo, V., and Colorni, A., 1996. “Ant system: optimization by a colony of cooperating agents,” IEEE Trans. Syst.,Man, Cybern. B, vol. 26, no. 1, pp. 29–41.
  9. Eberhart, R. C., Kennedy, J., 1995. “A New Optimizer Using Particle Swarm Theory.” Proceedings of the 6th International Symposium on Micro Machine and Human Science. Nagoya, Japan 39-432.
  10. Jang, J., Sun, C. and Mizutani, E., 1997. Neuro-fuzzy and Soft Computing, Prentice Hall, New York.
  11. Shi Y.H., Eberhart R.C. 1998. “A modified particle swarm optimizer.” in: Proc. of IEEE World Conf. on Computation Intelligence, 69–73.
Index Terms

Computer Science
Information Sciences

Keywords

TSK Fuzzy Modeling Subtractive Clustering Particle Swarm Optimization Permeability Log Data