Prediction of the Risk of Circumferential Stress Corrosion Cracking by Analysis of Digital Twins
Stress corrosion cracking is a well-recognised threat which can affect the risk of pipeline failure for susceptible pipelines, pipeline failures due to SCC are not unknown within industry. In general SCC risk factors are reasonably well understood, however predicting specific locations with high susceptibility to circumferential stress corrosion cracking (CSCC) remains difficult.
In an ideal scenario all pipelines would have appropriate coatings, cathodic protection and additional risk reduction measures. For new pipelines this is feasible. However, many pipelines in use today were installed in an era where SCC risk factors were not fully understood. It would not be economically feasible for example, to re-coat all pipelines. This results in a requirement for robust integrity management processes to target local inspection and remediation.
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.
As part of the digital strategy for integrity work undertaken by Penspen, the authors have applied advanced analytical techniques to develop digital twins of pipelines which are known to be at risk of CSCC to assist the development of a CSCC risk model.
By utilising modern computing capabilities combined with custom analysis coding, the authors have developed a methodology to predict and categorise the relative risk ranking posed by CSCC at an ultra-high resolution. In most cases resolution is substantially better than girth-weld basis.
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 assists the operator in the optimisation of limited resources.