Severe-slugging flow in offshore production flowlines and risers is undesirable and effective solutions are needed to prevent it. Automatic control using the top-side choke valve is a recommended anti-slug solution, but many anti-slug control systems are not robust against plant changes and inflow disturbances. The closed-loop system becomes unstable after some time, and the operators turn off the controller. In this thesis, the focus is on finding robust anti-slug solutions using both linear and nonlinear control approaches. The study includes mathematical modeling, analysis, OLGA simulations and experimental work.
First, a simplified dynamical four-state model was developed for a severeslugging pipeline-riser system. The new model, and five other models existing in the literature, were compared with results from the OLGA simulator. OLGA is a commercial package based on rigorous models and it is widely used in the oil industry. The new four-state model is also verified experimentally. Furthermore, the pipeline-riser model was extended to a well-pipeline-riser system by adding two new state variables.
Next, the simplified dynamical models were used for controllability analysis of the system. From the controllability analysis, suitable controlled variables and manipulated variables for stabilizing control were identified. A new mixed-sensitivity controllability analysis was introduced in which a single γ-value quantifies the robust performance of the control structure. In agreement with previous works, subsea pressure measurements were found to be the best controlled variables for an anti-slug control. The top-side valve is usually used as the manipulated variable, and two alternative locations also were considered. It was found that a subsea choke valve close to the riser base has the same operability as the top-side choke valve, while a well-head valve is not suitable for anti-slug control.
Three linear control solutions were tested experimentally. First, H∞ control based on the four-state mechanistic model of the system was applied. H∞ mixed-sensitivity design and H∞ loop-shaping design were conisidered for this. Second, IMC design based on a identified model was chosen. The resulting IMC controller is a second-order controller that can be implemented as a PID controller with a low-pass filter (PID-F). Finally, PI-control was considered and PI tuning values were obtained from the proposed IMC controller. It was shown that the IMC (PID-F) controller has good performance and robustness, matching the model-based H∞ controller, and it does not need a mechanistic model and it is easier to tune.
Four nonlinear control solutions were tested; three of them are based on the mechanistic model and the fourth one is based on identified models. The first solution is state feedback with state variables estimated by nonlinear observers. Three types of observers were tested experimentally and it was found that the nonlinear observers could be used only when the using the top-side pressure measurement. The second solution is an output-linearizing controller using two directly measured pressures. The third nonlinear controller is PI control with gain adaptation based on a simple model of the static gain. The last nonlinear solution is a gain-scheduling of three IMC controllers which were designed based on identified models. The gain-scheduling IMC does not need a mechanistic model and shows better robustness compared to the other nonlinear control solutions.