Stochastic adaptive learning rate in an identification method: an approach for on-line drilling processes monitoring

Authors:
A. Ba;S. Hbaieb;N. Mechbal;M. Vergé
Affiliations:
Laboratoire de Mécanique des Systèmes et des Procédés, UMR-CNRS, Ecole Nationale Supérieure d'Arts et Métiers, Paris, France and and Schlumberger Riboud Product Centr ...;Schlumberger Riboud Product Centre, Clamart Cedex, France;Laboratoire de Mécanique des Systèmes et des Procédés, UMR-CNRS, Ecole Nationale Supérieure d'Arts et Métiers, Paris, France;Laboratoire de Mécanique des Systèmes et des Procédés, UMR-CNRS, Ecole Nationale Supérieure d'Arts et Métiers, Paris, France
Venue:
ACC'09 Proceedings of the 2009 conference on American Control Conference
Year:
2009

Citing 4
Cited 0

System identification: theory for the user

System identification: theory for the user
Detection of abrupt changes: theory and application

Detection of abrupt changes: theory and application
Parameter adaptation in stochastic optimization

On-line learning in neural networks
State-space recursive least-squares with adaptive memory

Signal Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

On-line drilling processes monitoring is an essential task in enhancing their performances. In oilfield industry, dysfunctions that might occur have to be detected at the earliest possible stage in order to preserve drilling efficiency. This paper deals with a methodology for drilling processes monitoring by identifying time varying parameters. The basic idea behind the proposed algorithm is to improve the tracking ability of parameters change by means of an identification method using a new approach to adjust the forgetting factor. The effectiveness of the developed method is highlighted through experimental data obtained from tests campaign.