CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

An Imperialist Competitive Algorithm Artificial Neural Network Method to Predict Oil Flow Rate of the Wells

by Shahram Mollaiy Berneti, Mehdi Shahbazian
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 26 - Number 10
Year of Publication: 2011
Authors: Shahram Mollaiy Berneti, Mehdi Shahbazian
10.5120/3137-4326

Shahram Mollaiy Berneti, Mehdi Shahbazian . An Imperialist Competitive Algorithm Artificial Neural Network Method to Predict Oil Flow Rate of the Wells. International Journal of Computer Applications. 26, 10 ( July 2011), 47-50. DOI=10.5120/3137-4326

@article{ 10.5120/3137-4326,
author = { Shahram Mollaiy Berneti, Mehdi Shahbazian },
title = { An Imperialist Competitive Algorithm Artificial Neural Network Method to Predict Oil Flow Rate of the Wells },
journal = { International Journal of Computer Applications },
issue_date = { July 2011 },
volume = { 26 },
number = { 10 },
month = { July },
year = { 2011 },
issn = { 0975-8887 },
pages = { 47-50 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume26/number10/3137-4326/ },
doi = { 10.5120/3137-4326 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:12:28.013316+05:30
%A Shahram Mollaiy Berneti
%A Mehdi Shahbazian
%T An Imperialist Competitive Algorithm Artificial Neural Network Method to Predict Oil Flow Rate of the Wells
%J International Journal of Computer Applications
%@ 0975-8887
%V 26
%N 10
%P 47-50
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Flow rates of oil, gas and water are most important parameters of oil production that is detected by Multiphase Flow Meters (MFM). Conventional MFM collects data on long-term, because of the radioactive source is used for detection and in unmanned location used due to being away from wells. In this work, a new method based on feed-forward artificial neural network (ANN) and Imperialist Competitive Algorithm (ICA) have been proposed to predict of oil flow rate of the wells. The proposed algorithm combines the local searching ability of the gradient–based back-propagation (BP) strategy with the global searching ability of imperialist competitive algorithm. Imperialist Competitive Algorithm is used to decide the initial weights of the neural network. The ICA-ANN is applied to predict oil flow rate of the wells utilizing data set of 31 wells in one of the northern Persian Gulf oil fields of Iran. The performance of the ICA-ANN is compared with ANN and the results demonstrate the effectiveness of the ICA-ANN.

References
  1. Rallo, R., Ferre-Gin, J., Arenas, A., & Giralt, F. 2002. Neural virtual sensor for the inferential prediction of product quality from process variables. Computers and Chemical Engineering, 26, 1735–1754.
  2. Qu1, X., Feng, J. and Sun, W. 2008. Parallel Genetic Algorithm Model Based on AHP and Neural Networks for Enterprise Comprehensive Business, IEEE Intl. Conf. on Intelligent Information Hiding and Multimedia Signal Processing, pp.897-900.
  3. Souto, M.C.P.de, Yamazaki, A. and Ludernir, T.B. 2002. Optimization of neural network weights and architecture for odor recognition using simulated annealing, Proc. 2002 Intl. Joint Conf. on Neural Networks,Vol.1, pp.547-552.
  4. Reed, R. 1993. Pruning algorithms-a survey, IEEE Trans. Neural Networks Vol.4, pp.740–747.
  5. zhang, C., Shao, H. and Li, Y. 2000. Particle swarm optimization for evolving artificial neural network, 2000 IEEE Intl. Conf. on Systems, Man, and Cybernetics, Vol. 4, Oct. pp.2487 – 2490.
  6. Atashpaz-Gargari, E. Lucas, C. 2007. Imperialist Competitive Algorithm: An algorithm for optimization inspired by imperialistic competition. IEEE Congress on Evolutionary Computation, pp.4661–4667.
  7. Biabangard-Oskouyi, A., Atashpaz-Gargari, E., Soltani, N., Lucas, C. 2008. Application of Imperialist Competitive Algorithm for materials property characterization from sharp indentation test. To be appeared in the International Journal of Engineering Simulation.
  8. Rajabioun, R., Hashemzadeh, F., Atashpaz-Gargari, E., Mesgari, B., Rajaei Salmasi, F. 2008. Identification of a MIMO evaporator and its decentralized PID controller tuning using Colonial Competitive Algorithm. Accepted to be presented in IFAC World Congress.
  9. Atashpaz-Gargari, E., Hashemzadeh, F., Rajabioun, R. and Lucas, C. 2008. Colonial Competitive Algorithm, a novel approach for PID controller design in MIMO distillation column process, International Journal of Intelligent Computing and Cybernetics, 1 (3), 337–355.
  10. Sepehri Rad, H., Lucas, C. 2008. Application of Imperialistic Competition Algorithm in Recommender Systems. In: 13th Int'l CSI Computer Conference (CSICC'08), Kish Island, Iran.
  11. Hornik, K. Stinchcombe, M. and White, H. 1990. Universal approximation of an unknown mapping and its derivatives using multilayer feed forward networks. Neural Netw, 3(5):551–60.
  12. Hornick, K. Stinchcombe, M. and White, H.1989. Multilayer feed-forward networks are universal approximators. Neural Networks, 2, 359–366.
  13. Nash, J. E. Sutcliffe, J. V. 1970. River flow forecasting through conceptual models I: A discussion of principles. Journal of Hydrology, 10, 282–290.
Index Terms

Computer Science
Information Sciences

Keywords

Artificial Neural Network Back-Propagation Oil Flow Rate Multiphase Flow meter Imperialist Competitive Algorithm

Powered by PhDFocusTM