CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

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

by Edris Joonaki, Shima Ghanaatian, Ghassem Zargar
journal cover thumbnail

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
  1. Lumley D E, Behrens R A and Wang Z, 1997, Assessing the technical risk of a 4D seismic project, The Leading Edge, 161287–1292.
  2. Han, D. -H. andBatzle, M. L. , 2004, Gassmann's equation and fluid- saturation effects on seismic velocities, Geophysics, p. 398-405.
  3. Gassmann, F. , 1951, U ¨ber die Elastizita¨tporo¨serMedien. Vier. derNatur. Gesellschaft Zu¨rich, No. 96, P. 1-23. The English translation of this paper is available at http://sepwww. stanford. edu/sep/berryman/ PS/gassmann. pdf.
  4. Duffy, J. and Mindlin, R. D. , 1957. Stress-strain relations and vibrations of a granular medium: Journal of Applied Mechanics, V. 24, p. 585-593.
  5. Varela, O. J. , et al. , 2006, Using time-lapse seismic amplitude data to detect variations of pore pressure and fluid saturation due to oil displacement by water: a numerical study based on one-dimensional prestack inversion: Journal of Geophysics and Engineering, p. 177.
  6. Wood, A. W. , 1955, A Textbook of Sound. New York: McMillan Co.
  7. Smith, T. M. , et al. , 2003, Gassmann fluid substitutions-A tutorial: Geophysics, V. 68, No. 2, p. 430-440.
  8. Geertsma, J. and Smit, D. C. , 1961, Some aspects of elastic wave propagation in fluid-saturated porous solids: Geophysics, V. 26, No. 2, p. 169-181.
  9. Mindlin, R. D. , 1949, Compliance of elastic bodies in contact, Journal of Applied Mechanics, No. 16, p. 259-268.
  10. Andersen, C. F. and Johansen, T. A. , 2010, Test of rock physics models for prediction of seismic velocities in shallow unconsolidated sands: a well log data case: Geophysical Prospecting, V. 58, No. 6, p. 1083-1098.
  11. Vidal S, 2000, Integrating geomechanics and geophysics for reservoir seismic monitoring feasibility studies, SPE 65157, SPE Annual Technical Conference and Exhibition, Paris, France, October 24-25.
  12. Landrø M, 2001, Discrimination between pressure and fluid saturation changes from time-lapse seismic data, Geophysics, 66836-844.
  13. Murphy, W. F. , 1982, Effects of microstructure and pore fluids on the acoustic properties of granular sedimentary materials, Stanford University.
  14. Christensen N I and Wang H F, 1985, The Influence of pore pressure and confining pressure on dynamic elastic properties of Berea sandstone, Geophysics, 50207-213.
  15. Osdal, B. , O. Husby, H. A. Aronsen, N. Chen, and T. Alsos, 2006, Mapping the fluid front and pressure buildup using 4D data on Norne Field: The Leading Edge, 25, 1134–1141, doi:10. 1190/1. 2349818.
  16. Horne, S. and MacBeth, C. , 1994, Inversion for seismic anisotropy using genetic algorithms. Geophysical Prospecting 42(8), 953±974.
  17. Dal Moro, G. , Pipan, M, 2007, Joint Inversion of Surface Wave Dispersion Curves and Reflection Travel Times via Multiobjective Evolutionary Algorithms, Journal of Applied Geophysics, 61, 56-81.
  18. Boomer, K. , Brazier, R , 2009, Stochastic Modeling of the Velocity Structure: Beyond Joint Inversion Methods, 11th SAGA Biannual Technical Meeting and Exhibition, Switzerland, 16-18 September.
  19. Rwechungura, R. , Suwartadi, E. , Dadashpour, M. , Kleppe J. , and B. Foss, 2010, The Norne Field Case – A Unique Comparative Case Study, SPE 127538, the SPE intelligent energy conference and exhibition, Utrecht, The Netherlands, March 23-25.
  20. Dadashpour, M. , Echeverría Ciaurri, D. , Mukerji, T. , Kleppe, J. , Landrø, M. , A Derivative-Free Approach for the Estimation of Porosity and Permeability Using Time-Lapse Seismic and Production Data, SPE 2009.
Index Terms

Computer Science
Information Sciences

Keywords

Artificial intelligence Geophysics Porosity Permeability Norne oil field Reservoir characterizations

Powered by PhDFocusTM