Automated operational states detection for drilling systems control in critical conditions

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Authors: Veres, G.V. and Sabeur, Z.A.

Journal: ESANN 2013 proceedings, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

Pages: 53-58

ISBN: 9782874190810

Critical events in industrial drilling should be overcome by engineers while they maintain safety and achieve their targeted operational drilling plans. Geophysical drilling requires maximum awareness of critical situations such as "Kicks", "Fluid loss" and "Stuck pipe". These may compromise safety and potentially halt operations with the need of staff rapid evacuations from rigs. In this paper, a robust method for the detection of operational states is proposed. Specifically, Echo State Networks (ESNs) were benchmarked and tested rigorously despite the challenging unbalanced datasets used for training. Nevertheless, these challenges were overcome and led to acceptable ESNs performances.

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