In many deepwater pipeline applications, a clear understanding of pipe local buckling behaviour due to excessive bending and/or external pressure is required to adequately design subsea pipeline systems to an acceptable degree of risk. With ultra-deepwater pipelines being considered for water depths of nearly 3000 m, pipe collapse, in many instances, will govern design. For example, bending loads imposed on the pipeline near the seabed (sagbend region) during installation will reduce the external pressure resistance of the pipeline, and this design case will influence (and generally govern) the final selection of an appropriate pipeline wall thickness. To date, the deepest operating pipelines have been laid using the J-lay method, where the pipeline departs the lay vessel in a near-vertical orientation, and the only bending condition resulting from installation is near the touchdown point in the sagbend. More recently, however, the S-lay method is being considered for installation of pipelines to water depths of nearly 2800 m. During deepwater S-lay, the pipeline originates in a horizontal orientation, bends around a stinger located at the stern or bow of the vessel, and then departs the lay vessel in a near-vertical orientation. Thus, during S-lay, the installed pipe experiences bending around the stinger (overbend region), followed by combined bending and external pressure in the sagbend region. In view of these bending and external pressure loading conditions, analytical work was performed to better understand the local buckling behaviour of thick-walled linepipe due to bending, and the influence of bending on pipe collapse. Variables considered in the analytical evaluations include pipe material properties, geometric properties, pipe thermal treatment, the definition of critical strain, and imperfections such as ovality and girth weld offset. Presented is the finite element analysis (FEA) model developed to perform the assessments, validation of the model, the results of analyses and correlations to experimental data.

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