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Fuzzy classifier fusion: an application to reservoir facies identification

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Abstract

An application of classifier fusion technique is presented to improve the performance of automated reservoir facies identification system. The algorithm presented in this study uses three well-known classifiers, namely Bayesian, k-nearest neighbor (kNN), and support vector machine (SVM) to automatically identify four defined facies of Asmari Formation from log-derived amplitude versus offset (AVO) attributes. Fuzzy Sugeno integral (FSI) method is then employed to combine the outputs of three investigated classifiers and increase the consistency of reservoir facies identification process. The experimental results obtained from applying the presented algorithm on data related to three wells drilled in Asmari Formation provide evidence of the effectiveness of the proposed algorithm regarding true positive (TP), false positive (FP), and classification accuracy criteria.

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Acknowledgements

The authors would like to acknowledge the financial support of University of Tehran for this research under Grant Number (22708/1/02).

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Authors and Affiliations

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

    Amir Mollajan & Hossein Memarian

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

    Majid Nabi-Bidhendi

Authors
  1. Amir Mollajan
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  2. Hossein Memarian
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  3. Majid Nabi-Bidhendi
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Correspondence to Amir Mollajan.

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Mollajan, A., Memarian, H. & Nabi-Bidhendi, M. Fuzzy classifier fusion: an application to reservoir facies identification. Neural Comput & Applic 30, 825–834 (2018). https://doi.org/10.1007/s00521-016-2690-0

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  • DOI: https://doi.org/10.1007/s00521-016-2690-0

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