Design and Comparison of Constrained MPC With PID Controller for Heave Disturbance Attenuation in Offshore Managed Pressure Drilling Systems

Nikoofard, Amirhossein; Johansen, Tor Arne; Mahdianfar, Hessam; Pavlov, Alexey

doi:10.4031/mtsj.48.2.4

Cited by 19 publications

(17 citation statements)

References 28 publications

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“…Instead of identifying and tracking all these components and placing a complex adaptive controller in the feedback loop, we aim to identify the dominant frequency of the disturbance and attenuate its effect using the proposed adaptive output regulator. Similar to , we assume the disturbance spectrum is represented by JONSWAP with significant wave height, H s =10 m , the modal period, T 0 =12.08 s , the peak frequency,

ω_{0} = 0.52 \frac{italicrad}{s}

, and γ = 3.3. This spectrum is approximated by a second‐order wave transfer function

H (s) = \frac{2 λ_{h} ω_{0} σs}{s^{2} + 2 λ_{h} ω_{0} s + ω_{0}^{2}}

, with λ h =0.1017, and σ = 4.89.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…In , two different disturbance rejection strategies, a nonlinear output regulation controller and a linear internal model controller, based on discretized partial differential equations are presented. In , a constrained model predictive controller is designed for handling heave disturbance and output regulation constraints. Later, Model Predictive Control (MPC) was used also in by assuming full knowledge of heave disturbance in the so‐called MPD Heave Lab .…”

Section: Introductionmentioning

confidence: 99%

“…Although heave disturbance on floaters due to wave motions is inherently uncertain, and one cannot expect to know its frequency content precisely, all the cited works (except ) on heave disturbance challenge assume that the frequency of the disturbance is known. The heave disturbance model applied in is stochastic (second‐order filtered Gaussian white noise) describing sea waves in the north sea with assumption of finite water depth and limited fetch. As mentioned before, in MPD applications, downhole pressure should be kept within certain upper and lower bounds, which transforms into time‐domain performance objectives for the control system.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive output regulation for offshore managed pressure drilling

Mahdianfar

Pavlov

2016

Adaptive Control & Signal

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This paper deals with output regulation problem for an offshore managed pressure drilling system subject to sinusoidal disturbances. The disturbance is caused by the heave motion of the floating drilling rig during pipe connections, and the objective is to maintain a constant pressure at the bottom of the well. The controller for the heave disturbance attenuation consists of three cascaded parts. First, a nonlinear inversion element is applied to invert nonlinearity of choke. Second, an adaptive compensator is designed based on internal model, and certainty equivalence principles for asymptotic rejection of time-varying heave disturbance. Third, an output feedback controller is synthesized for providing stability and improving transient performance of closed-loop system. Robust stability of closed-loop system is analyzed using edge theorem. Simulation results of the combined adaptive output regulator are presented, which show satisfactory set-point tracking and attenuation of the heave disturbance. ‡ Annulus is the space between the drill-pipe and casings. For uncased parts of the well, annulus is the space between the drill-pipe and formation. In Figure 1, the drilling fluid after leaving the drill-bit enters and flows up the annulus. § Rotating control device is used during drilling operations for making a rotating seal around the drill pipe. It isolates the well from the surrounding environment and helps to keep the pressures inside the well confined. ¶ Wellbore is the drilled hole, including the uncased part of the well. || The MPD Heave Lab is a medium scale (900 m well) experimental facility at NTNU, tailor made for testing control strategies for the heave problem.The model described by equations (3)-(9) is in form of a nonlinear strict feedback system, with an unmatched sinusoidal disturbance. By applying the following nonlinear inversion H f D 1 0 0 :

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ω_{0} = 0.52 \frac{italicrad}{s}

, and γ = 3.3. This spectrum is approximated by a second‐order wave transfer function

H (s) = \frac{2 λ_{h} ω_{0} σs}{s^{2} + 2 λ_{h} ω_{0} s + ω_{0}^{2}}

, with λ h =0.1017, and σ = 4.89.…”

Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Adaptive output regulation for offshore managed pressure drilling

Mahdianfar

Pavlov

2016

Adaptive Control & Signal

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“…Proportional and integral gains are chosen as 0.01 and 0.0002, respectively. For more details on how the PI controller can be designed and tuned for this scenario the interested reader is referred to [33], [34], [35]. The parameter values for the UKF are the same as previous scenario.The measured bottom-hole pressure (p bh ) and choke pressure (p c ) for pipe connection scenario with PI controller is illustrated in Figure 10.…”

Section: Case 2: Pipe Connectionmentioning

confidence: 99%

State and Parameter Estimation of a Drift-Flux Model for Underbalanced Drilling Operations

Nikoofard

Aarsnes

Johansen

et al. 2017

IEEE Trans. Contr. Syst. Technol.

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Abstract-We consider a drift-flux model (DFM) describing multiphase (gas-liquid) flow during drilling. The DFM uses a specific slip law which allows for transition between single and two phase flows. With this model, we design Unscented and Extended Kalman Filters (UKF and EKF) for estimation of unmeasured state, production, and slip parameters using real time measurements of the bottom-hole pressure, outlet pressure, and outlet flow-rate. The OLGA high-fidelity simulator is used to create two scenarios from Under-Balanced Drilling (UBD) on which the estimators are tested: A pipe connection scenario and a scenario with a changing production index. A performance comparison reveals that both UKF and EKF are capable of identifying the production indices of gas and oil from the reservoir into the well with acceptable accuracy, while the UKF is more accurate than the EKF. Robustness of the UKF and EKF for the pipe connection scenario is studied in case of uncertainties and errors in the reservoir and well parameters of the model. It is found that these methods are very sensitive to errors in the reservoir pore pressure value. However, they are robust in the presence of error in the liquid density value of the model.

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“…The drilling fluid carries cuttings from the drill bit to the surface. In conventional (over-balanced) drilling, or managed pressure drilling (MPD), the pressure in the well is kept greater than pressure of reservoir to prevent influx from entering the well (Nikoofard et al (2014c(Nikoofard et al ( , 2013). But in UBD operations, the pressure of the well is kept below the pressure of the reservoir, allowing formation fluid flow into the well during the drilling operation.…”

Section: Under Balanced Drillingmentioning

confidence: 99%

Evaluation of Lyapunov-based Adaptive Observer using Low-Order Lumped Model for Estimation of Production Index in Under-balanced Drilling

2015

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A distributed drift-flux model and a low-order lumped model describing a multiphase (gas-liquid) flow in the well during Under-Balanced Drilling (UBD) has been presented. This paper presents a novel nonlinear adaptive observer to estimate the total mass of gas and liquid in the annulus and production constant of gas and liquid from the reservoir into the well during UBD operations. Furthermore, it describes a joint unscented Kalman filter to estimate parameters and states for both the distributed drift-flux and lumped model by using real-time measurements of the choke and the bottom-hole pressures. The performance of the adaptive observers are evaluated for typical drilling scenarios. The results show that all adaptive observers are capable of identifying the production index, although the adaptive observers based on the low-order lumped model achieves better convergence rate than adaptive observer based on the drift-flux model. The results show that the LOL model is sufficient for the purpose of estimating the production parameters.

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Design and Comparison of Constrained MPC With PID Controller for Heave Disturbance Attenuation in Offshore Managed Pressure Drilling Systems

Cited by 19 publications

References 28 publications

Adaptive output regulation for offshore managed pressure drilling

Adaptive output regulation for offshore managed pressure drilling

State and Parameter Estimation of a Drift-Flux Model for Underbalanced Drilling Operations

Evaluation of Lyapunov-based Adaptive Observer using Low-Order Lumped Model for Estimation of Production Index in Under-balanced Drilling

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