Control-Oriented Modeling for Managed Pressure Drilling Automation Using Model Order Reduction

Lordejani, Sajad Naderi; Besselink, Bart; Abbasi, Mohammad Hossein; Kaasa, Glenn-Ole; Schilders, W.H.A.; Wouw, Nathan van de

doi:10.1109/tcst.2020.2994535

Cited by 10 publications

(6 citation statements)

References 26 publications

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“…1. The flow behavior in each of these pipes is then modeled by a set of isothermal Euler equations [26], [8] of the following form (see [12] and [8] and references therein):…”

Section: Mathematical Modeling For Mpdmentioning

confidence: 99%

“…As discussed in [12], pressure control systems for MPD are conventionally designed to be slow to, among other things, avoid such stability issues. As such, these controllers are also slow in kick and mud loss attenuation.…”

Section: Introductionmentioning

confidence: 99%

“…Low-to medium-resolution spatial discretization is another model reduction approach which has been pursued in drilling automation [10,14,16,17]. A recently developed approach to constructing low-order, but accurate, hydraulics models is to apply automatic model order reduction techniques to the models resulted from the high-resolution spatial discretization [12,18,19]. A new perspective to this problem has been presented in our recent preliminary work [20], the rationale of which is explained next.…”

Section: Introductionmentioning

confidence: 99%

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Model complexity reduction and controller design for managed pressure drilling automation

Lordejani

Besselink

Schilders

et al. 2023

Journal of Process Control

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“…1. The flow behavior in each of these pipes is then modeled by a set of isothermal Euler equations [26], [8] of the following form (see [12] and [8] and references therein):…”

Section: Mathematical Modeling For Mpdmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Model complexity reduction and controller design for managed pressure drilling automation

Lordejani

Besselink

Schilders

et al. 2023

Journal of Process Control

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“…Kassa initially proposed a fundamental mathematical model outlining the drilling system's essential phenomena in [2]. Following this, several authors have utilized the model for state estimation and control of annular pressure in wells [3,4]. In an early application of MPD, the authors in [5,6] utilized MPD techniques to cement a seven-inch liner on the Gullfaks field.…”

Section: Introductionmentioning

confidence: 99%

Advance Optimized Nonlinear Control Strategies for Manage Pressure Drilling

Rehman,

Ghias,

Ahmad

et al. 2024

IEEE Access

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Manage Pressure Drilling (MPD) presents a pioneering methodology for enhancing control over well operations and drilling processes, furnishing a proactive approach to mitigate risks stemming from fluctuating downhole pressures. Through the meticulous regulation of drilling fluid via a choke valve, MPD adeptly manages fluid pressure dynamics, effectively addressing challenges posed by undesirable pressure fluctuations. These fluctuations, often instigated by the vertical oscillations of the drill string due to wave disturbances, known as Heave Motion, can jeopardize the integrity of the well. MPD thus serves as a robust contingency against weather-induced delays, optimizing operational efficiency and bolstering the effectiveness of offshore drilling endeavors. This paper introduces various nonlinear control techniques, notably Sliding Mode Control (SMC) and Super-Twisting Sliding Mode Control (STSMC), with a specific emphasis on their application. The central objective is to utilize these controllers to effectively manage the choke valve and mitigate pressure fluctuations within the well. The controller gains underwent detailed refinement using a genetic algorithm, with a focus on minimizing the integral time absolute error (ITAE). The mathematical analysis involves using Lyapunov stability criteria to confirm that the introduced controllers are asymptotically stable. Additionally, comprehensive performance comparisons among the proposed control strategies were meticulously conducted using MATLAB/Simulink simulations. Moving beyond simulation, real-time validation involved an experimental setup integrating the capabilities of the C2000 Delfino with the MCU F28379D Launchpad.INDEX TERMS Manage Pressure Drilling, heave reduction, super twisting sliding mode control, controller-in-loop.

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“…A vast literature [9], [10], [11] has focused on reducing the dimensionality of complex dynamical systems. The fundamental approach of model reduction is to generate a lower order model that preserves key features of the original system, see e.g., [12], [13], [14], [15], [16], [17].…”

Section: Introductionmentioning

confidence: 99%

Exact Decomposition of Optimal Control Problems via Simultaneous Block Diagonalization of Matrices

Nazerian

Bhatta

Sorrentino

2023

IEEE Open J. Control. Syst.

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In this paper, we consider optimal control problems (OCPs) applied to large-scale linear dynamical systems with a large number of states and inputs. We attempt to reduce such problems into a set of independent OCPs of lower dimensions. Our decomposition is 'exact' in the sense that it preserves all the information about the original system and the objective function. Previous work in this area has focused on strategies that exploit symmetries of the underlying system and of the objective function. Here, instead, we implement the algebraic method of simultaneous block diagonalization of matrices (SBD), which we show provides advantages both in terms of the dimension of the subproblems that are obtained and of the computation time. We provide practical examples with networked systems that demonstrate the benefits of applying the SBD decomposition over the decomposition method based on group symmetries.

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Control-Oriented Modeling for Managed Pressure Drilling Automation Using Model Order Reduction

Abstract: Control-oriented modelling for managed pressure drilling automation using model order reduction. IEEE

Cited by 10 publications

References 26 publications

Model complexity reduction and controller design for managed pressure drilling automation

Model complexity reduction and controller design for managed pressure drilling automation

Advance Optimized Nonlinear Control Strategies for Manage Pressure Drilling

Exact Decomposition of Optimal Control Problems via Simultaneous Block Diagonalization of Matrices

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