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

Automation of Managed Pressure Drilling (MPD) enables fast and accurate pressure control in drilling operations. The performance that can be achieved by automated MPD is determined by, firstly, the controller design and, secondly, the hydraulics model that is used as a basis for controller design. On the one hand, such hydraulics model should be able to accurately capture essential flow dynamics, e.g., wave propagation effects, for which typically complex models are needed. On the other hand, a suitable model should be simple enough to allow for extensive simulation studies supporting well scenario analysis and high-performance controller design. In this paper, we develop a model order reduction approach for the derivation of such a control-oriented model for {single-phase flow} MPD {operations}. In particular, a nonlinear model order reduction procedure is presented that preserves key system properties such as stability and provides guaranteed (accuracy) bounds on the reduction error. To demonstrate the quality of the derived control-oriented model, {comparisons with field data and} both open-loop and closed-loop simulation-based case studies are presented

How to recognise a kick : A cognitive task analysis of drillers’ situation awareness during well operations

Acknowledgements This article is based on a doctoral research project of the first author which was sponsored by an international drilling rig operator. The views presented are those of the authors and should not be taken to represent the position or policy of the sponsor. The authors wish to thank the industrial supervisor and the drilling experts for their contribution and patience, as well as Aberdeen Drilling School for allowing the first author to attend one of their well control courses.Peer reviewedPostprin

Adaptive PI Control of Bottom Hole Pressure during Oil Well Drilling

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Design, development and control of a managed pressure drilling setup

Drilling in challenging conditions require precise control over hydrodynamic parameters for safer and efficient operation in oil and gas industries. Automated managed pressure drilling (MPD) is one of such drilling solution which helps to maintain operational parameters effectively over conventional drilling technique. The main goal is to maintain bottomhole pressure between reservoir formation pressure and fracture pressure with kick mitigation ability. Real life MPD system has to confront nonlinearity induced by drilling fluid rheology and flow parameters. To obtain a better understanding of this operation, a lab scale experimental setup has been developed. Reynolds number and pressure drop per unit length were considered to obtain hydrodynamic similarity. A vertical concentric pipe arrangement has been used to represent the drill string and annular casing region. A linearized gain switching proportional integral (PI) controller and a nonlinear model predictive controller (NMPC) have been developed to automate the control operation in the experimental setup. A linearizer has been designed to address the choke nonlinearity. Based on the flow and pressure criteria, a gain switching PI controller has been developed which is able to control pressure and flow conditions during pipe extension, pump failure and influx attenuation cases. On the other hand, a nonlinear Hammerstein-Weiner model has been developed which assists in bottomhole pressure estimation using pump flow rate and choke opening. The identified model has been integrated with a NMPC algorithm to achieve effective control within predefined pressure and flow constraints. Lastly, a performance comparison has been provided between the linearized gain switching PI controller and NMPC controller

Advanced control of managed pressure drilling

Automation of managed pressure drilling (MPD) enhances the safety and increases efficiency of drilling and that drives the development of controllers and observers for MPD. The objective is to maintain the bottom hole pressure (BHP) within the pressure window formed by the reservoir pressure and fracture pressure and also to reject kicks. Practical MPD automation solutions must address the nonlinearities and uncertainties caused by the variations in mud flow rate, choke opening, friction factor, mud density, etc. It is also desired that if pressure constraints are violated the controller must take appropriate actions to reject the ensuing kick. The objectives are addressed by developing two controllers: a gain switching robust controller and a nonlinear model predictive controller (NMPC). The robust gain switching controller is designed using H1 loop shaping technique, which was implemented using high gain bumpless transfer and 2D look up table. Six candidate controllers were designed in such a way they preserve robustness and performance for different choke openings and flow rates. It is demonstrated that uniform performance is maintained under different operating conditions and the controllers are able to reject kicks using pressure control and maintain BHP during drill pipe extension. The NMPC was designed to regulate the BHP and contain the outlet flow rate within certain tunable threshold. The important feature of that controller is that it can reject kicks without requiring any switching and thus there is no scope for shattering due to switching between pressure and flow control. That is achieved by exploiting the constraint handling capability of NMPC. Active set method was used for computing control inputs. It is demonstrated that NMPC is able to contain kicks and maintain BHP during drill pipe extension