Abstract
This paper presents two approaches of Artificial Immune System for Pattern Recognition (CLONALG and Parallel AIRS2) to classify automatically the well drilling operation stages. The classification is carried out through the analysis of some mud-logging parameters. In order to validate the performance of AIS techniques, the results were compared with others classification methods: neural network, support vector machine and lazy learning.
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Serapião, A.B.S., Mendes, J.R.P., Miura, K. (2007). Artificial Immune Systems for Classification of Petroleum Well Drilling Operations. In: de Castro, L.N., Von Zuben, F.J., Knidel, H. (eds) Artificial Immune Systems. ICARIS 2007. Lecture Notes in Computer Science, vol 4628. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73922-7_5
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DOI: https://doi.org/10.1007/978-3-540-73922-7_5
Publisher Name: Springer, Berlin, Heidelberg
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