Nonlinear model predictive control of managed pressure drilling

Nandan, Anirudh; Imtiaz, Syed

doi:10.1016/j.isatra.2017.03.013

Cited by 21 publications

(5 citation statements)

References 20 publications

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“…LMPC is inadequate for these highly nonlinear systems . Under such circumstances, nonlinear MPC (NMPC) has received much interest from researchers and has been applied in many nonlinear processes. − But the implementation of NMPC is still faced with inherent limitations. Much effort is needed to get an accurate nonlinear model for designing a nonlinear controller, which is a cumbersome task.…”

Section: Introductionmentioning

confidence: 99%

A New Adaptive Factor-Based Output Compensation Strategy for Offset Free Control of MLD–MPC with Model–Plant Mismatch

Song

et al. 2021

Ind. Eng. Chem. Res.

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The mixed logical dynamical (MLD) model is a powerful framework of multimodel combinations for nonlinear systems control. Although superior control performance can be obtained by MLD-MPC, model–plant mismatch (MPM) will deteriorate the control performance. To eliminate the offset caused by MPM, a new adaptive factor-based output compensation strategy for MLD-MPC is proposed. The adaptive factor is introduced to guarantee better control performance of the MLD-MPC system. On the basis of Lyapunov stability theory and dynamics of the MLD-MPC system, the adaptive factor can be solved. By implementing the proposed strategy, the stability and zero offset of the MLD-MPC system can be achieved. Furthermore, the influence of submodel switching is considered in this new strategy, and the strategy is easy to carry out online. Finally, the proposed strategy is applied in a nonlinear benchmark processCSTR. The simulation results have demonstrated that the proposed method is effective and can satisfy the overall control performance.

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Section: Introductionmentioning

confidence: 99%

A New Adaptive Factor-Based Output Compensation Strategy for Offset Free Control of MLD–MPC with Model–Plant Mismatch

Song

et al. 2021

Ind. Eng. Chem. Res.

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“…The work considers underbalanced drilling which is a variant of the MPD, allowing reservoir production during drilling. The work of Nandan and Imtiaz (2017) includes an NMPC for switching between tracking of BHP and kick-attenuation in the wellbore, showing promising results by using the choke valve for control when performing pipe connections.…”

Section: Introductionmentioning

confidence: 99%

Identification and Optimal Control for Surge and Swab Pressure Reduction While Performing Offshore Drilling Operations

Tengesdal¹,

Holden²

2020

MIC

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In this paper, an unscented Kalman filter (UKF) coupled with a nonlinear model-predictive controller (NMPC) for a hydraulic wellbore model with multi-variable control and tracking is presented. In a wellbore, high drill string velocities in operational sequences such as tripping might result in surge and swab pressures in the annular section of the wellbore. To overcome these challenges, a controller incorporating safety and actuator limits should be used. A second-order model is used to predict axial drill string velocity downhole. With a NMPC specifying the block position trajectory, choke flow reference, desired backpressure pump flowrate and stand-pipe pressure, we can automatically supervise and control the pressure in the wellbore. To compensate for unmeasured states, an estimator is designed to predict the frictional pressure forces in the wellbore and filter noisy measurements. A stochastic approach for the hydraulic model is taken, including variance of the average fluctuations for the flow and pressure states. Comparing three NMPC configurations, the result of using an integration of the tracking error in the prediction model gave best offset-free tracking of the bottom-hole pressure. The controller compensates for the unknown fluctuations, and is shown to be robust towards model mismatch. Including the mechanical system in the NMPC prediction model, we can effectively constrain the predicted axial drill string velocity to reduce the pressure oscillations and achieve tracking of bottom hole pressure and choke differential pressure. The outcome is shown through extensive simulations to be an effective control strategy, reducing the pressure spikes while tripping.

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“…Nevertheless, a simplified model is often accepted for use in the MPC in order to address practical limitations relating to such things as incomplete understanding of the physical process, limited computational power, or inadequate availability of expertise for control system maintenance, often trading optimal process control for usability [13]. Indeed, in many MPC applications, models include simplifications of some type, such as reduction in model order or linearization of significant nonlinear processes (see [9,10,14,15,16,17,18,19]). The simplest of these models may be termed gray box models, which incorporate linear transfer function models and nonlinearity blocks to describe relationships between manipulated variables and system behavior.…”

Section: Introductionmentioning

confidence: 99%

Model predictive control and estimation of managed pressure drilling using a real-time high fidelity flow model

Park¹,

Price²,

Pixton³

et al. 2020

ISA Transactions

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When drilling an oil or gas well, well pressures may be controlled using a technology called managed pressure drilling. This technology often relies on model predictive control schemes; however, practical limitations have generally led to the use of simplified controller models that do not optimally handle certain perturbations in the physical system. The present work reports on the first implementation of a highly accurate system model that has been adapted for real-time use in a controller. This real-time high-fidelity model approximates the results of offline high-fidelity models without requiring operation by model experts. The effectiveness of the model is demonstrated through simulation studies of controller behavior under various drilling conditions, including an evaluation of the impact of sparse downhole feedback measurements.

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Nonlinear model predictive control of managed pressure drilling

Cited by 21 publications

References 20 publications

A New Adaptive Factor-Based Output Compensation Strategy for Offset Free Control of MLD–MPC with Model–Plant Mismatch

A New Adaptive Factor-Based Output Compensation Strategy for Offset Free Control of MLD–MPC with Model–Plant Mismatch

Identification and Optimal Control for Surge and Swab Pressure Reduction While Performing Offshore Drilling Operations

Model predictive control and estimation of managed pressure drilling using a real-time high fidelity flow model

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