Combining Traditional and Novel Approaches to Manage the Evolving Threat from Geohzards
Proceedings Publication Date
Andrew Wilde
Andrew Wilde, Oscar Ivan Gomez Rosso, Vasileios Athanasiou
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Pipelines are exposed to a range of loads throughout their operational life, some of which can be easily quantified and managed and others that develop at locations that may not have been predicted at the design stage and, if not proactively managed, can ultimately lead to failure. Ongoing technology developments, data analysis improvements and advanced survey techniques have led to a wealth of information that can be used to manage the geohazard threats that pipelines are exposed to, but in order to avoid pipeline damage and ultimately failures, improvements are required with regards to optimizing the available data, combining methods and selecting the best management strategy for a particular threat.

Technologies and survey methods are available to monitor changes affecting the stability of the soil in the pipeline right of way and for monitoring the direct effect of external loading on the pipeline. Information relating to pipeline movement can be supplemented by information relating to localized pipeline deformation (e.g., ovality, wrinkling) and also by identifying pipeline sections that may be more susceptible to axial strains (e.g., older pipe sections with poorer girth weld performance or pipelines with higher diameter-to-wall thickness ratios).

In this paper, the common effects of ground movement, including landslides and seismic activity, are reviewed and a methodology for the evaluation of geohazard loading based on bending strain and pipeline movement data is presented. The importance of understanding the effects of coincident threats in terms of a potential reduction in strain capacity is discussed with a particular focus on how combined evaluation of pipeline curvature data and high-resolution caliper data can provide an improved understanding of the probability of failure. The methodology is applied to two case studies to demonstrate how multiple data sets can be combined and used to prioritize locations for preventative action.

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