Abstract

Inline inspections (ILIs) are one of the most effective methods for managing the integrity of pipelines. However, many older pipelines were not designed to accommodate ILI tools. Pipeline operators often prioritize which pipelines to make inspectable on a risk-basis. While this risk-based approach has many merits, it does not necessarily result in the maximum risk reduction for a given budget as the risk-reduction from completing the inspection is not considered. An optimized prioritization strategy should consider both the uninspected risk and amount of risk reduction.

Since post-ILI risk are calculated based on the detected imperfections, it is not possible to directly calculate the risk-reduction from performing a first-time ILI. To overcome this, TC Energy (TCE) completed an exploratory analysis of numerous first-time ILI results to identify key parameters and built machine learning models which predicts the risk impact of performing first-time ILIs.

Several machine learning algorithms (neural network, decision tree, etc.) were trained on data from pre and post-ILI risk results from TCE’s quantitative risk assessment. The models were trained at a dynamic segment level and aggregated to an ILI assessment path evaluation. The best-performing machine learning model was selected that accurately predicts the risk reduction achieved from a first-time ILI.

These results demonstrate the risk-reduction of a first-time ILI can be accurately predicted before the inspection is performed. Combining the traditional risk-based prioritization approach with the predictive abilities to estimate risk-reduction will allow TCE to optimize the selection of first-time inspections by maximizing the amount of risk reduction.

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