Good flow and pressure control is essential for successful Underbalanced Drilling (UBD) operations. This work evaluates the use of Model Predictive Control (MPC) for integrated control of well conditions and the topside separation system during UBD. The downhole well pressure, separator liquid levels, and the separator pressure are controlled by manipulation of the rig pump, the choke, and the separator valves. The control system adheres to downhole and topside constraints. These constraints include pore and collapse pressures, minimum flow rate for hole cleaning, maximum choke pressure, separator pressure, and separator liquid levels. The proposed MPC solution uses simple Hammerstein-Wiener models, where parameters are determined by system identification incorporated into standard drilling procedures. The control system is tested using a high-fidelity multi-phase flow simulator (OLGA) for some common drilling scenarios, including drilling into a producing formation and performing connections. We show that the MPC solution is able to take proactive action to ensure safe and efficient operation without having to enter well control mode or shutting down the separator system. By limiting the amount and variation in influx from the reservoir, we get less Non-Productive Time (NPT), we improve safety, and we may to some extent be able to reduce the footprint of the equipment.
Summary
This work presents a new multivariable controller for management of topside and bottomhole objectives during underbalanced drilling (UBD). A model predictive control (MPC) solution is used to control pressures, rate of penetration (ROP), and flow downhole while also ensuring that the topside processing constraints are respected.
With automated control, it is possible to reduce nonproductive time (NPT), improve safety, and operate closer to the process constraints. MPC is a good fit for UBD because of its easy inherent handling of multiple objectives and constraints. With good pressure control, it is in some cases also possible to reduce the number of casing strings.
The control solution is evaluated through simulations in a high-fidelity multiphase-flow oil and gas simulator (OLGA). It is shown that we can meet multiple objectives both at the surface and at different locations in the well. The optimization problem is solved with good results well within the given time constraints.
The used linear prediction models are relatively easy to understand and maintain. They are also fast and well-suited for optimization and predictions. However, the process is nonlinear, and the linear models will be less accurate as the process conditions change. Retuning or model adaptation might be required to obtain the desired performance. It is possible to include nonlinear models in the control framework, referred to as nonlinear MPC (NMPC), but this will add complexity and require more computational power.
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