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

Proceedings Publication Date:

01 Apr 2021
Presenter
Aidan Charlton
Presenter
Company
Author
Aidan Charlton, Mohammod Rahman
Part of the proceedings of
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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