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Permeability prediction and construction of 3D geological model: application of neural networks and stochastic approaches in an Iranian gas reservoir

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Abstract

Determination of petrophysical parameters by using available data has a specific importance in exploration and production studies for oil and gas industries. Modeling of corrected permeability as a petrophysical parameter can help in decision making processes. The objective of this study is to construct a comprehensive and quantitative characterization of a carbonate gas reservoir in marine gas field. Artificial neural network is applied for prediction of permeability in accordance with other petrophysical parameters at well location. Correlation coefficient for this method is 84 %. In the study, the geological reservoir model is developed in two steps: First, the structure skeleton of the field is constructed, and then, reservoir property is distributed within it by applying new stochastic methods. Permeability is modeled by three techniques: kriging, sequential Gaussian simulation (SGS) and collocated co-simulation using modeled effective porosity as 3D secondary variable. This paper enhances the characterization of the reservoir by improving the modeling of permeability through a new algorithm called collocated co-simulation. Kriging is very simple in modeling the reservoir permeability, and also, original distribution of the data changes considerably in this model. In addition, the SGS model is noisy and heterogeneous, but it retains the original distribution of the data. However, the addition of a 3D secondary variable in third method resulted in a much more reliable model of permeability.

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Acknowledgments

This study was carried out under the supervision and permission of NIOC-Exploration Directorate. The authors would like to thank Mr. A. E. Mirmortazavi and Mr. S. A. Miri for their support in publishing this paper.

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Authors and Affiliations

  1. University College of Engineering, University of Tehran, Tehran, Iran

    Asaad Fegh & Gholam Hosein Norouzi

  2. Institute of Geophysics, University of Tehran, Tehran, Iran

    Mohammad Ali Riahi

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  1. Asaad Fegh
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  2. Mohammad Ali Riahi
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  3. Gholam Hosein Norouzi
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Correspondence to Asaad Fegh.

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Fegh, A., Riahi, M.A. & Norouzi, G.H. Permeability prediction and construction of 3D geological model: application of neural networks and stochastic approaches in an Iranian gas reservoir. Neural Comput & Applic 23, 1763–1770 (2013). https://doi.org/10.1007/s00521-012-1142-8

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  • DOI: https://doi.org/10.1007/s00521-012-1142-8

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