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Landslide Monitoring & Effect Modelling by Analysis of Digital Twins Built With Minimal Data Input

Abstract

Ground movements and the consequent effects on pipeline integrity are well acknowledged
by industry. Failure modes such as stress corrosion cracking is a well-recognised threat which
can affect the risk of pipeline failure for susceptible pipelines. The changes in pipeline stresses induced by ground movement can impact initiation and progression of SCC in addition to
having the potential to cause pipeline failure by simple exceedance of limit states.

In an ideal scenario all pipelines would have appropriate coatings, cathodic protection and
equally importantly; additional risk reduction measures such as proper monitoring of areas
susceptible to ground movement. For new pipelines this is feasible. However, many pipelines
in use today were installed before modern sensor technology became available. It would not
be economically feasible for example, to retrofit all pipelines with modern sensor technology
other than in specific locations. This results in a requirement for robust integrity management
processes.

While the average prices of oil and gas remain low the pressure to reduce cost of additional
measures will remain high. One of the ways in which the pipeline industry can maintain a safe
transportation system with a finite level of resource, is to implement intelligent risk-based
integrity and inspection strategies which are combined with state of the art data analysis
techniques.

As part of the digital strategy for integrity work undertaken by Penspen, the authors have
applied predictive analytics and additional techniques to digital twins of pipelines which are
known to be at risk due to the potential for ground movements.

Whole pipeline networks can be rapidly risk categorised in ultra-high resolution by applying
the risk model to subsequent digital twins. Ultimately this can help to recommend specific
locations for further detailed inspection which can assist the operator in the optimisation of
limited resources.

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