ARTICLE

Reservoir porosity determination from 3D seismic data – Application of two machine learning techniques

ISHEH ALIMORADI1 ALI MORADZADEH1 MOHAMMAD REZA BAKHTIARI2,3
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1 Department of Mining, Petroleum and Geophysics Engineering, Shahrood University of Technology, Shahrood, Iran.,
2 Department of Petroleum Engineering, Amir Kabir University of Technology (Tehran Polytechnic), Tehran, Iran.,
3 National Iranian Oil Company, Exploration Directorate, Tehran, Iran.,
JSE 2012, 21(4), 323–345;
Submitted: 1 February 2012 | Accepted: 9 September 2012 | Published: 1 December 2012
© 2012 by the Authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
Abstract

Alimoradi, A., Moradzadeh, A. and Bakhtiari, M.R., 2012. Reservoir porosity determination from 3D seismic data - application of two machine learning techniques. Journal of Seismic Exploration, 21: 323-345. This paper proposes a method for solving 3D seismic data inversion problems for prediction of porosity in hydrocarbon reservoirs. An actual carbonate oil field in the south-western part of Iran was selected for this study. Taking real geological conditions into account, different synthetic models of reservoir were constructed for a range of viable porosity values. Seismic surveying was performed next on these models. From seismic response of the synthetic models, a large number of seismic attributes were identified as candidates for porosity estimation. Classes of attributes such as energy, instantaneous, and frequency attributes were included amongst others. Applying sensitivity analysis, the two most significant attributes were determined as Envelope Weighted Phase and Envelope Weighted Frequency, which were subsequently used in our machine learning algorithms. In particular, we used feed-forward artificial neural networks (FNN) and support vector regression machines (SVR) to develop relationships between the known synthetic attributes and synthetic porosity values in a given setting. The FNN consists of six neurons in a single hidden layer and the SVR method uses a Gaussian radial basis function. Compared with real values from the well data, we observed that SVM outperforms FNN due to its better handling of noise and model complexity.

Keywords
seismic inversion
seismic attributes
synthetic data
feed forward neural network
support vector machine
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Journal of Seismic Exploration, Electronic ISSN: 0963-0651 Print ISSN: 0963-0651, Published by AccScience Publishing