Shale Gas Rock Properties Prediction using Artificial Neural Network Technique and Multi Regression Analysis, an example from a North American Shale Gas Reservoir

M. Reza Rezaee; Roger M. Slatt; Richard F. Sigal

doi:10.1071/ASEG2007ab120

ISSN: 2202-0586
E-ISSN:

oa Shale Gas Rock Properties Prediction using Artificial Neural Network Technique and Multi Regression Analysis, an example from a North American Shale Gas Reservoir
Authors M. Reza Rezaee^a, Roger M. Slatt^b and Richard F. Sigal^c
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^a Department of Petroleum Engineering, Curtin University of Technology, , Perth, WA, [email protected] ^b School of Geology and Geophysics, Oklahoma University, , OK, , USA, , [email protected] ^c School of Petroleum and Geol. EngineeringOklahoma University, , OK, , USA, , [email protected]
Publisher: Australian Society of Exploration Geophysicists
Source: ASEG Extended Abstracts, Volume 2007, Issue ASEG2007 - 19th Geophysical Conference, Dec 2007, p. 1 - 4
DOI: https://doi.org/10.1071/ASEG2007ab120
- Published online: 01 Dec 2007

Abstract

Summary

Estimation of reservoir parameters has always been a challenge for shale gas reservoirs. This study has concentrated on neural network technique and multiple regression analysis to predict reservoir properties including porosity, permeability, fluid saturation and total organic carbon content from conventional wireline log data for a large North American shale gas reservoir. More than 262 core analysis data from 3 wells were used as “target” and “response” for neural network and multiple regression analysis. Common log data available in three wells including GR, SP, RHOB, NPHI, DT and deep resistivity were used as “input” and “predictor”.

This study shows that reservoir parameters could be better estimated using the neural network technique than through multiple regression. The neural network method had a correlation coefficient greater than 80% for most of the parameters. Although providing a set of algorithms, multiple regression analysis was less successful for predicting reservoir parameters.

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2026-01-20

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References

Rezaee, M.R., Kadkhodaie Ilkhchi, A., and Mohammad Alizadeh, P., 2007. Intelligent approaches for synthesizing of petrophysical logs. J. Geophys. Eng. (in press).
Rezaee, M.R., Kadkhodaie Ilkhchi, A., and Barabadi, A., 2007, Prediction of shear wave velocity from petrophysical data utilizing intelligent systems: An example from a sandstone reservoir of Carnarvon Basin. Journal of Petroleum Science and Engineering, 55, 201-212.
Rezaee, M.R., Jafari1, A., and Kazemzadeh, E., 2006, Relationships between permeability, porosity and pore throat size in carbonate rocks using regression analysis and neural networks. J. Geophys. Eng. 3, 370–376.
Kadkhodaie Ilkhchi1, A., Rezaee, M.R., and Moallemi, A., 2006, A fuzzy logic approach for estimation of permeability and rock type from conventional well log data: an example from the Kangan reservoir in the Iran Offshore Gas Field. J. Geophys. Eng. 3, 356–369.
Rezaee, M.R., Nikjoo, M., Movahhed, B., and Sabeti, N., 2006, Prediction of effective porosity and water saturation from wireline logs using artificial neural network technique. Journal of Geological Society of Iran, 1, 21-27.
Rezaee, M.R., Slatt, R., and Kadkhodaie Ilkhchi, A., 2006, Application of Intelligent Systems for Generating Wireline Logs. Earth Science Journal, School of Geology and Geophysics, University of Oklahoma.

/content/journals/10.1071/ASEG2007ab120

Article Type: Research Article

Keyword(s): Artificial Neural Network; Multi Regression Analysis; North American; Shale Gas reservoir

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oa Shale Gas Rock Properties Prediction using Artificial Neural Network Technique and Multi Regression Analysis, an example from a North American Shale Gas Reservoir

Abstract

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