A Streamlined Multidiscipline Workflow for Pipeline Slugging Assessment

Abstract

Abstract Free spans exist in subsea multiphase pipelines laid over undulating seabed profiles or across continental scarps for offshore field developments. Slug flow induced fatigue damage on the free spans can potentially lead to pipeline re-sizing, more restrictive installation and welding requirements, and limits on life-of-field operational flexibility; these, in turn, have a significant impact on project economics. Slug flow assessments are typically associated with large uncertainties (e.g. flow conditions and span arrangement) and therefore require comprehensive sensitivity analysis to confirm robustness; this can prove time-consuming. This presents opportunities for significant optimisation in workflows to target governing operating scenarios. This paper describes an integrated iterative approach between the Flow Assurance and Pipeline engineering disciplines to streamline the workflow based on the value or cost associated with changes in input parameters that impact pipeline fatigue assessment outcomes. Case studies on two multiphase pipelines (one with multiple long spans introduced by undulating seabed profile and another featuring a very long span across a continental scarp) are presented to illustrate this design approach. The results show that early identification of the key pipeline profile features and dominating spans for pipeline slugging fatigue assessments facilitated the Flow Assurance slug flow modelling optimisation and reduced the computational time. Early engagement and alignment regarding pipeline assessment methods and desired slugging data requirements were very valuable for optimising the Flow Assurance slug flow modelling fidelity and also to minimise data processing requirements for both Flow Assurance and Pipeline disciplines. Timely feedback from the Pipeline discipline regarding fatigue accumulation based on screening work performed using an initial slugging data set with a wide flow rate range helped narrow down additional slug flow scenarios to be modelled by Flow Assurance.