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Evaluating distribution pattern of petrophysical properties and their monitoring under a hybrid intelligent based method in southwest oil field of Iran

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Abstract

This study deals with reservoir characterization based on well log data using an unsupervised self-organizing map (SOM) and supervised neural network algorithms with the aim of clustering log responses into reservoir facies of an oil field located in southwest of Iran. In order to promote and justify the quality control and quantify spatial relationships for petrophysical properties, some of neural network-based approaches were introduced such as the SOMs as the intelligent clustering method compared with other hybrid methods, principal component analysis networks (PCANs) and multilayer perceptron (MLP) and statistical clustering (CA) methods. The results obtained from all the abovementioned methods are compared to each other, and the best option is selected based on accuracy and capabilities of clustering and estimation of the petrophysical data, concluding that for predicting any characteristic of the reservoirs, the appropriate network should be chosen and a unique network cannot be convenient for all of them. Accordingly, the SOM clustering technique was employed to classify the reservoir rocks. Based on the SOM visualization, the reservoir rocks were classified into six facies associated with specific petrophysical properties; among them, F6 expressed the best reservoir quality which is characterized by the low amount of density, highest DT, high amount of neutron porosity (NPHI), and lowest GR response. Ultimately, the performance of all the methods was compared to estimate the porosity and permeability within each facies. The results revealed the preference and reliability of PCAN in predicting porosity and confirmed the capability of MLP in permeability prediction. This study also indicates that neuro-prediction of formation properties using well log data is a feasible methodology for optimization of exploration programs and reduction of expenditure by delineating potentially oil-bearing strata with higher accuracy and lower expenses. The resulting neural net-based model can be used as a powerful and distributive system to reduce the high impact of risk in similar fields.

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Acknowledgements

The authors gratefully acknowledge their special thanks to the Research and Development Management Office of the National Iranian Oil Company due to offering of the required data and information. Our sincere gratitude is given to Dr. S.A.Tabatabaei, senior officer of the former head department of NIOC. We also would like to acknowledge our sincere thanks to anonymous reviewers for their constructive comments.

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Authors and Affiliations

  1. Department of Mining Engineering, Ahar Branch, Islamic Azad University, Ahar, Iran

    Aynur Nasseri

  2. Faculty of Mining Engineering, Sahand University of Technology, Tabriz, Iran

    Mohammad Jafar Mohammadzadeh

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  1. Aynur Nasseri
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  2. Mohammad Jafar Mohammadzadeh
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Correspondence to Aynur Nasseri.

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Nasseri, A., Mohammadzadeh, M.J. Evaluating distribution pattern of petrophysical properties and their monitoring under a hybrid intelligent based method in southwest oil field of Iran. Arab J Geosci 10, 9 (2017). https://doi.org/10.1007/s12517-016-2766-2

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