Real time model identification using multi-fidelity models in managed pressure drilling. (2nd February 2017)

• Record Type: Journal Article
• Authors: Eaton, Ammon N.; Beal, Logan D.R.; Thorpe, Samuel D.; Hubbell, Casey B.; Hedengren, John D.; Nybø, Roar; Aghito, Manuel
• Is Part Of: Computers & chemical engineering. Volume 97(2017)
• Page Start: 76
• Page End: 84
• Publication Date: 2017-02-02
• Subjects: Drilling automation -- Nonlinear model predictive control -- Switched control -- High fidelity control models -- Managed pressure drilling; Chemical engineering -- Data processing -- Periodicals; 660.0285
• DOI: 10.1016/j.compchemeng.2016.11.008
• Languages: English
• ISSNs: 0098-1354

Abstract

Abstract : Highlights: Real time control model identification without interrupting the process. Use of high fidelity simulator predictions in feedback control. Application of the novel control scheme to managed pressure drilling. Improved noise rejection with an ℓ1 -norm objective for estimation and control. Enhanced controller robustness with a switched control scheme. Abstract: Highly accurate model predictions contribute to the performance and stability of model predictive control. However, high fidelity models are difficult to implement in real time control due to the large and often nonconvex optimization problem that must be completed within the feedback cycle time. To address this issue, a switched control scheme that uses high fidelity model predictions in real time control is presented. It uses real time simulated data to identify a linear empirical control model. The real time model identification procedure does not interrupt the process, and is suitable for nonlinear processes where offline model identification is difficult. Controller stability is discussed, and the control scheme is demonstrated in a managed pressure drilling simulation. The switched controller provides improved performance over both a high fidelity model based controller and a nonadaptive empirical model.