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Classification of Petroleum Well Drilling Operations with a Hybrid Particle Swarm/Ant Colony Algorithm

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Next-Generation Applied Intelligence (IEA/AIE 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5579))

Abstract

This paper describes an investigation of the hybrid PSO/ACO algorithm to classify automatically the well drilling operation stages. The method feasibility is demonstrated by its application to real mud-logging dataset. The results are compared with bio-inspired methods, and rule induction and decision tree algorithms for data mining.

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References

  1. Kyllingstad, A., Horpestad, J.L., Klakegg, S., Kristiansen, A., Aadnoy, B.S.: Factors Limiting the Quantitative Use of Mud-Logging Data. In: Proc. of the SPE Asia Pacific Oil and Gas Conference, Singapore (1993)

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Author information

Authors and Affiliations

  1. UNESP/IGCE/DEMAC, C.P. 178, CEP 13506-900, Rio Claro (SP), Brazil

    Adriane B. S. Serapião

  2. UNICAMP/FEM/DEP, C.P. 6122, CEP 13081-970, Campinas (SP), Brazil

    José Ricardo P. Mendes

Authors
  1. Adriane B. S. Serapião
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  2. José Ricardo P. Mendes
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Information Engineering, National University of Tainan, 700, Tainan, Taiwan

    Been-Chian Chien

  2. Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-Pei Hong

  3. Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan

    Shyi-Ming Chen

  4. Department of Computer Science, Texas State University-San Marcos, 601 University Drive, 78666-4616, San Marcos, TX, USA

    Moonis Ali

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© 2009 Springer-Verlag Berlin Heidelberg

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Serapião, A.B.S., Mendes, J.R.P. (2009). Classification of Petroleum Well Drilling Operations with a Hybrid Particle Swarm/Ant Colony Algorithm. In: Chien, BC., Hong, TP., Chen, SM., Ali, M. (eds) Next-Generation Applied Intelligence. IEA/AIE 2009. Lecture Notes in Computer Science(), vol 5579. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02568-6_31

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  • DOI: https://doi.org/10.1007/978-3-642-02568-6_31

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02567-9

  • Online ISBN: 978-3-642-02568-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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