文章基本信息

标题：Downhole Pressure Soft-Sensing using Interacting Multiple Modeling ∗
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作者：Bruno F. Riccio ; Alex F. Teixeira ; Bruno O.S. Teixeira 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2015
卷号：48
期号：6
页码：298-303
DOI：10.1016/j.ifacol.2015.08.047
语种：English
出版社：Elsevier
摘要：AbstractIn this work we design data-driven soft sensors of downhole pressure for gas-lift oil wells. We employ a two-step procedure. First, discrete-time (N)ARX models are identified offline from historical data. Second, recursive predictions of these multiple models are combined with current measured data (of variables other than the downhole pressure) by means of an interacting bank of (unscented) Kalman filters. We investigate the usage (i) of linear versus nonlinear models and (ii) of models with or without seabed variables in addition to platform variables. Results are validated by means of experimental data from three oil wells.
关键词：KeywordsSoft sensorssystem identificationinteracting multiple modelsdownhole pressure