Model-Free Predictive Anti-Slug Control of a Well-Pipeline-Riser

Christer Dalen,David Di Ruscio

doi:10.4173/mic.2016.1.4

Abstract

Simplied linearized discrete time dynamic state space models are developed for a 3-phase well-pipelineriser and tested together with a high delity dynamic model built in K-Spice and LedaFlow. In addition the Meglio pipeline-riser model is used as an example process. These models are developed from a subspace algorithm, i.e. Deterministic and Stochastic system identication and Realization (DSR), and implemented in a Model Predictive Controller (MPC) for stabilizing the slugging regime. The MPC, LQR and PI control strategies are tested.

Highlights

Severe-slugging is a problem regarding well-pipelineriser processes in the offshore industry and is characterized by significant flow rate and pressure oscillations observed at the topside choke
One solution, which is regarded as the most costeffective, is to introduce active feedback where we define the topside choke valve as the manipulative variable and some pressure, flow rate or density measurements as the controlling variable
To maximize the goal variable a controller needs to be designed to operate around an open-loop unstable working point, here the largest possible choke opening which stabilizes the system may be defined as a performance measure of the controller

Summary

Introduction

Severe-slugging is a problem regarding well-pipelineriser processes in the offshore industry and is characterized by significant flow rate and pressure oscillations observed at the topside choke. One solution, which is regarded as the most costeffective, is to introduce active feedback where we define the topside choke valve as the manipulative variable and some pressure, flow rate or density measurements as the controlling variable. Demonstrations of Model-Free Predictive Control (MFPC) is performed on the 3 state Di Meglio model (Di Meglio et al (2009)) and on the K-Spice/LedaFlow simulator (KSpice, LedaFlow). A complete example which introduces the constraints leads to a discrete time linear model, of both the input rate of change and the input amplitude can be found in Section 3.2 and Appendix A in Di Ruscio (2013). ∆xk xk y ∈ R := Bottom-riser pressure, [bar] , State observer u ∈ R := Topside choke @ {0.15, 0.20} [1]

Di Meglio model

Discussion y

Findings

Discussion and summary