Real Time Model Identification Using Multi-Fidelity Models in Managed Pressure Drilling

Keywords

Drilling automation, Nonlinear model predictive control, Switched control, High fidelity control models, Managed pressure drilling

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.

Original Publication Citation

Ammon N. Eaton, Logan D. R. Beal, Samuel D. Thorpe, Casey B. Hubbel, John D. Hedengren, Roar Nybø, Manuel Aghito. (2017). Real time model identification using multi-fidelity models in managed pressure drilling. Computers & Chemical Engineering, 97. https://www.sciencedirect.com/science/article/pii/S0098135416303428

Document Type

Peer-Reviewed Article

Publication Date

2017-02-02

Permanent URL

http://hdl.lib.byu.edu/1877/4035

Publisher

Elsevier

Language

English

College

Ira A. Fulton College of Engineering and Technology

Department

Chemical Engineering

University Standing at Time of Publication

Associate Professor

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