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An Intelligence Approach for Porosity and Permeability Prediction of Oil Reservoirs using Seismic Data

by Edris Joonaki, Shima Ghanaatian, Ghassem Zargar
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 80 - Number 8
Year of Publication: 2013
Authors: Edris Joonaki, Shima Ghanaatian, Ghassem Zargar
10.5120/13881-1778

Edris Joonaki, Shima Ghanaatian, Ghassem Zargar . An Intelligence Approach for Porosity and Permeability Prediction of Oil Reservoirs using Seismic Data. International Journal of Computer Applications. 80, 8 ( October 2013), 19-26. DOI=10.5120/13881-1778

@article{ 10.5120/13881-1778,
author = { Edris Joonaki, Shima Ghanaatian, Ghassem Zargar },
title = { An Intelligence Approach for Porosity and Permeability Prediction of Oil Reservoirs using Seismic Data },
journal = { International Journal of Computer Applications },
issue_date = { October 2013 },
volume = { 80 },
number = { 8 },
month = { October },
year = { 2013 },
issn = { 0975-8887 },
pages = { 19-26 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume80/number8/13881-1778/ },
doi = { 10.5120/13881-1778 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:54:00.994660+05:30
%A Edris Joonaki
%A Shima Ghanaatian
%A Ghassem Zargar
%T An Intelligence Approach for Porosity and Permeability Prediction of Oil Reservoirs using Seismic Data
%J International Journal of Computer Applications
%@ 0975-8887
%V 80
%N 8
%P 19-26
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Nowadays the main challenge is to obtain a method for the estimation of key reservoir parameters with the lowest possible estimation error. Accurate reservoir characterization requires the integration of core and log data to understand the variation in hydraulic properties such as porosity, permeability and capillary pressure. Time-lapse seismic can be used as an important tool in reservoir characterization, monitoring and management. Reservoir parameters are converted to seismic parameters by using the rock physics models. This paper presents an analysis and explanation of an approach of developing rock physics model, and explains how the input data can be obtained to the model. And also this study presents an intelligence approach for the oil reservoir characterization by using seismic elastic properties and rock physics model together with minimum estimation error.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Artificial intelligence Geophysics Porosity Permeability Norne oil field Reservoir characterizations

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