Real Time Model Identification Using Multi-Fidelity Models in Managed Pressure Drilling
Drilling automation, Nonlinear model predictive control, Switched control, High fidelity control models, Managed pressure drilling
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
BYU ScholarsArchive Citation
Eaton, Ammon; Beal, Logan; Thorpe, Sam; Hubbell, Casey; Hedengren, John; Nybø, Roar; and Aghito, Manuel, "Real Time Model Identification Using Multi-Fidelity Models in Managed Pressure Drilling" (2017). Faculty Publications. 2098.
Ira A. Fulton College of Engineering and Technology
© 2017 Elsevier
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