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Stochastic identification using the maximum likelihood method and a statistical reduction: application to drilling dynamics

Abstract : A drill-string is a slender structure that drills rock to search for oil. The nonlinear interaction between the bit and the rock is of great importance for the drill-string dynamics. The interaction model has uncertainties, which are modeled using the nonparametric probabilistic approach. This paper deals with a procedure to perform the identification of the dispersion parameter of the probabilistic model of uncertainties of a bit-rock interaction model. The bit-rock interaction model is represented by a nonlinear constitutive equation, and the identification of the parameter of this probabilistic model is carried out using the Maximum Likelihood method together with a statistical reduction in the frequency domain using the Principal Component Analysis.
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Submitted on : Tuesday, May 1, 2012 - 6:09:36 PM
Last modification on : Thursday, March 19, 2020 - 11:52:03 AM
Long-term archiving on: : Thursday, August 2, 2012 - 2:22:48 AM

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  • HAL Id : hal-00692970, version 1

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T.G. Ritto, Christian Soize, R. Sampaio. Stochastic identification using the maximum likelihood method and a statistical reduction: application to drilling dynamics. MECOM 2010 (IX Argentinean Congress on Computational Mechanics y II South American Congress on Computational Mechanics) and CILAMCE 2010 (XXXI Iberian-Latin-American Congress on Computational Methods in Engineering), Nov 2010, Buenos Aires, Argentina. pp.ISSN 1666-6070, Pages: 1-11. ⟨hal-00692970⟩

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