Design Drivers and Potential Solutions for Slugging Induced Fatigue of Steel Lazy Wave Risers

Abstract Slug flow-induced fatigue has emerged as a governing criterion in the design of deep-water steel lazy wave production risers (SLWR). Even though an SLWR performs similarly to or better than a Steel Catenary Riser (SCR) for wave-induced fatigue, fatigue sources like slugging-induced fatigue and VIV generated fatigue may drive its fatigue design. International standards like DNV-ST-F201 [13] and API-RP-2RD [12] recommend addressing slugging loads for compliant riser systems. Such standards do not provide a clear methodology for slugging behavior evaluation of deepwater riser systems. Dynamic loads induced by the passage of a slug (series of liquid slug followed by gas pockets) through the riser cause significant fatigue damage in the lower region of the SLWR such as the touchdown point (TDP) and the hog bend. The selection of the SLWR configuration (and material selection in some cases) will be driven by these loads. This paper discusses these drivers using a slug flow-induced fatigue estimation methodology. To gain confidence in the slugging fatigue methodology and determining the slugging drivers, the current industry standard software OrcaFlex was validated using ABAQUS software in conjunction with an in-house user subroutine ABASLUG. The subroutine offers more refined and realistic slug flow data, which leads to more faithful slug induced fatigue prediction. In classic OrcaFlex slugging model, time series of slug/bubble trains representing the slug flows for different zones of SLWR (such as riser top, mid riser, sag bend, hog bend and touchdown zone (TDZ), etc.) are applied to the corresponding sections, respectively. In the ABAQUS approach, variations of mixture density with time at each element along the riser are captured and applied by user subroutine ABASLUG. The time-varying density approach in ABAQUS proved superior to the classic OrcaFlex model, and a need for such a utility in OrcaFlex was born. With this new learning, an in-house solver Slug-In was developed, which brings the time domain density utility into OrcaFlex. Design recommendations are proposed; mitigation measures such as topside choking or cladding the critical welds are also discussed. Sensitivities of slug flow parameters such as slug/bubble density and length ratios on fatigue performance are evaluated. The effect of various SLWR configuration parameters such as hang-off angle, buoyancy length and buoyancy factors are presented. The impact of governing mechanical design parameters like riser wall thickness, material strength, and use of special material and weld quality are also investigated.