Exploring the Use of Field Data to Improve Predictions of Pipeline As-Laid Embedment

Reliable prediction of as-laid embedment for surface-laid offshore pipelines is important for predicting seabed resistance during operation. However, the accuracy of existing methods can be limited by various challenges: difficulties in determining representative, very near-surface soil properties (either by field or laboratory investigation), inherent seabed variability including bathymetry, and changing/ complex lay conditions. Given significant experience across the offshore sector within stalling offshore pipelines, this study explores the potential to predict as-laid embedment directly from field cone penetration test (CPT) results. Part of the ARC ITRH Transforming energy Infrastructure through Digital Engineering (TIDE), the installation data for two parallel flowlines from a single projecton the Northwest Shelf of Australia are used to show that correlations can be made between cone resistance and pipeline embedment – if the cone data is used appropriately. By comparing the observation based correlations with predictions made using current industry practice, the potential to develop a data informed method to predict pipeline as-laid embedment is demonstrated – with the current study to now be extended to other sites, and (if relevant) to consider pReliable prediction of as-laid embedment for surface-laid offshore pipelines is important for predicting seabed resistance during operation. However, the accuracy of existing methods can be limited by various challenges: difficulties in determining representative, very near-surface soil properties (either by field or laboratory investigation), inherent seabed variability including bathymetry, and changing/ complex lay conditions. Given significant experience across the offshore sector with installing offshore pipelines, this study explores the potential to predict as-laid embedment directly from field cone penetration test (CPT) results. Part of the ARC ITRH Transforming energy Infrastructure through Digital Engineering (TIDE), the installation data for two parallel flowlines from a single projecton the Northwest Shelf of Australia are used to show that correlations can be made between cone resistance and pipeline embedment – if the cone data is used appropriately. By comparing the observation based correlations with predictions made using current industry practice, the potential to develop a data informed method to predict pipeline as-laid embedment is demonstrated – with the current study to now be extended to other sites, and (if relevant) to consider parameters other than cone resistance to improve the approach.