Abstract
Oil well drilling towers have different operating modes during a real operation, like drilling, tripping, and reaming. Each mode involves certain external disturbances and uncertainties. In this study, using the nonlinear model for the modes of the operation, robust and/or adaptive control systems are designed based on the models. These control strategies include five types of controllers: cascaded proportional–integral–derivative, active disturbance rejection controller, loop shaping, feedback error learning, and sliding mode controller. The study presents the design process of these controllers and evaluates the performances of the proposed control systems to track the reference signal and reject the uncertain forces including the parametric uncertainties and the external disturbances. This comparison is based on the mathematical performance measures and energy consumption. In addition, three architectures are presented to control the weight on bit during drilling process, and also to maintain a preset constant weight on bit, two control approaches are designed and presented.
Highlights
A drilling rig is a machine that digs boreholes in the ground
The integral square error (ISE), integral absolute error (IAE), integral of time weighted absolute error (ITAE), and root mean square error (RMSE) values are derived from the simulation results and the performance of the controllers are evaluated
The ISE, which integrates the square of the error over time, is associated with the error energy
Summary
A drilling rig is a machine that digs boreholes in the ground. The drilling machine has different equipment like actuator, draw-works, traveling block or hook, mud pump, top-drive, drill string, and bottom-holeassembly (BHA). Dynamic modeling of these equipment constitutes the basis for system analysis and control. The model has to describe the system’s behavior during operating modes in real wells and it has to be simple enough for analysis and control purposes. As the model uncertainties (structured and/or unstructured) have strong effects on the nonlinear control systems,[1] the robust and/or adaptive control architectures can be applied to deal with model uncertainties.
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