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Development of a Probabilistic Model for Stress Corrosion Cracking of Underground Pipelines Using Bayesian Networks: A Concept

[+] Author Affiliations
Swati Jain, Francois Ayello, John A. Beavers, Narasi Sridhar

Det Norske Veritas USA, Inc., Dublin, OH

Paper No. IPC2012-90340, pp. 615-625; 11 pages
doi:10.1115/IPC2012-90340
From:
  • 2012 9th International Pipeline Conference
  • Volume 4: Pipelining in Northern and Offshore Environments; Strain-Based Design; Risk and Reliability; Standards and Regulations
  • Calgary, Alberta, Canada, September 24–28, 2012
  • Conference Sponsors: International Petroleum Technology Institute, Pipeline Division
  • ISBN: 978-0-7918-4515-8
  • Copyright © 2012 by ASME

abstract

Stress corrosion cracking (SCC) continues to be a safety concern, mainly because it can remain undetected before a major pipeline failure occurs. SCC processes involve complex interactions between metallurgy, stress, external soil environment, and the electrolyte chemistry beneath disbonded coatings. For these reasons, assessing SCC failure probability at any given location on a pipeline is difficult.

In an attempt to assess the SCC probability, a Bayesian network model was created. The model links events by cause-consequence connections. The strengths of these connections are adjusted using expert knowledge, analytical models, and data from the field. Bayesian network modeling was chosen because it takes into account the high degree of uncertainty in the input parameters. Other models have been developed to assess SCC: such as indexing methods, heuristics models, and mechanistic models. However, their main limitation is the uncertainty of the input parameters. One other strength of the Bayesian model is that calculations can be run in two directions: the forward direction from cause to consequence and the backward direction from observation to causative factors. In the forward direction, the model evaluates the probability of SCC failure using various input probabilities of factors that are important to SCC. In the backward direction, the model can evaluate the effect of any known occurrence of SCC failure on the probabilities of causative factors and thus condition the Bayesian network to evaluate the future failure probability.

In this paper, we discuss a Bayesian network model for high-pH SCC. The conceptual framework, acquisition of data, and the inclusion of uncertainties are described. In addition, an example of the model application to high pH SCC is given. The effects of service and field conditions such as soil type, soil chemistry, coating type, surface preparation techniques, stresses, residual stress due to pipe manufacturing conditions, welds, dents, location such as proximity to rivers, wetting and drying cycles, etc. on the SCC probability can be assessed with the model. The model details shown in this publication will only cover the stress affect due to surface preparation, welds, dents, and manufacturing conditions and temperature effect. The effects of other factors and validation against field experience will be discussed in future publications.

Copyright © 2012 by ASME

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