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Prediction of Relative Permeability for Multiphase Flow in Fractured Oil Reservoirs by using a Soft Computing Approach

by Edris Joonaki, Shima Ghanaatian
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 73 - Number 16
Year of Publication: 2013
Authors: Edris Joonaki, Shima Ghanaatian
10.5120/12829-0286

Edris Joonaki, Shima Ghanaatian . Prediction of Relative Permeability for Multiphase Flow in Fractured Oil Reservoirs by using a Soft Computing Approach. International Journal of Computer Applications. 73, 16 ( July 2013), 45-55. DOI=10.5120/12829-0286

@article{ 10.5120/12829-0286,
author = { Edris Joonaki, Shima Ghanaatian },
title = { Prediction of Relative Permeability for Multiphase Flow in Fractured Oil Reservoirs by using a Soft Computing Approach },
journal = { International Journal of Computer Applications },
issue_date = { July 2013 },
volume = { 73 },
number = { 16 },
month = { July },
year = { 2013 },
issn = { 0975-8887 },
pages = { 45-55 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume73/number16/12829-0286/ },
doi = { 10.5120/12829-0286 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:40:18.993794+05:30
%A Edris Joonaki
%A Shima Ghanaatian
%T Prediction of Relative Permeability for Multiphase Flow in Fractured Oil Reservoirs by using a Soft Computing Approach
%J International Journal of Computer Applications
%@ 0975-8887
%V 73
%N 16
%P 45-55
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Artificial neural networks (ANNs) are weightily parallels, distributed processors, constituting of numerous simple processing units that are used to solve the complex problems. In this paper ANN was used to present complex relation between water-oil relative permeability key points and rock and fluid properties for multiphase flow in porous media. In this research 200 relative permeability curves from Iranian carbonate were used to reach the ultimate goal. 6 key points which contains end points and the crossover points, were considered for each curve. ANN was then used to predict these key points from different rock and fluid properties. ANN presents very high correlation coefficients in the range of 0. 85 to 0. 95 for Kr key points. The results proved that ANN is an appropriated tool to predict water-oil relative permeability in porous media with high accuracy when the needed core and fluid properties are available.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Soft computing Artificial Neural Network (ANN) Water-oil relative permeability Multiphase flow

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