Data analytics for drilling operational states classifications
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Authors: Veres, G.V. and Sabeur, Z.A.
Journal: 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2015 - Proceedings
This paper provides benchmarks for the identification of best performance classifiers for the detection of operational states in industrial drilling operations. Multiple scenarios for the detection of the operational states are tested on a rig with various drilling wells. Drilling data are extremely challenging due to their non-linear and stochastic natures, notwithstanding the embedded noise in them and unbalancing. Nevertheless, there is a possibility to deploy robust classifiers to overcome such challenges and achieve good automated detection of states. Three classifiers with best classification rates of drilling operational states were identified in this study.