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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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
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