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Intelligent Corrosion Prediction using Bayesian Belief Networks

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Accurate knowledge of corrosion location severity cause and growth rate is critical to pipeline integrity and in‑line inspection (ILI) is widely regarded as the most reliable and convenient method of obtaining such knowledge. Much industry effort has therefore centred on improving the metal loss detection and sizing capabilities of ILI tools.However when ILI data are lacking or unattainable operators must seek alternative ways to monitor the integrity of an asset. For managing internal corrosion Internal Corrosion Direct Assessment (ICDA) is perhaps the best known alternative. ICDA employs the engineering analyses of corrosion and flow modelling to identify areas at high risk from internal corrosion. The highest priority areas are excavated and directly examined in order to establish the condition of the pipeline. This combination of corrosion and flow modelling can be used to provide detailed corrosion predictions but in the absence of ILI data selection of excavation sites can be problematic. The inherent randomness and uncertainty in the models means that the outputs must often be overly conservative; consequently ICDA can be a costly process with no guarantee of quality.The shortcomings of ICDA (and related methods) create a need for a more reliable and accurate corrosion prediction solution which does not require a pipeline to be inspected using ILI. This paper explores the use of Bayesian Belief Networks (BBNs) for this purpose. BBNs are graphical models capable of integrating expert knowledge and data into a single system; ‘expert knowledge’ is captured through industry standard corrosion modelling techniques while ‘data’ is captured through historical ILIs for piggable pipelines. A trained BBN can then be used to make predictions for pipelines without ILI data based on a knowledge of their operational conditions alone.Using case studies on real pipelines it is demonstrated that BBNs can lead to more intelligent predictions of internal corrosion behaviour and improved pipeline integrity management decisions.

Product Number: 51319-13372-SG
Author: Michael Smith
Publication Date: 2019
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