3 research outputs found
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
An optimum control-based approach for Dynamic Positioning of vessels
This paper presents a solution to the problem of Dynamic Positioning of vessels in Arctic environments, using a finite-horizon optimal control based approach. As the first step, an Unscented Kalman Filter (UKF) based non-linear observer is developed for estimating both the vessel states and unknown inputs such as ice load. To perform better set point control and disturbance rejection, a Non-linear Model Predictive Controller (NMPC) is employed for dynamic positioning. Using the developed estimation and control strategies, successful simulation results are obtained. � 2016 IEEE.Scopu