Manual inspection data are often collected by an operator for its non-piggable pipelines to determine the state of the pipeline infrastructure across its portfolio of upstream operating locations. This inspection effort confirms the current understanding of corrosion mechanisms and the success of ongoing protective measures. With sufficient data for a pipeline, statistical estimation of the extreme wall loss value and the percentage of the pipeline that would have wall thickness less than designated important thresholds can be undertaken.

The principle difficulty in the process for estimating a worst case for corrosion is validating an appropriate statistical distribution for use in the computations. A database of wall loss measurements from in-line inspection (ILI) provided the basis for a clever data-based solution. Statistical sampling and estimation were used to create datasets for which a number of different distributions could be evaluated. The results of this exercise were used to build a statistical distribution selection algorithm that was calibrated versus statistics that could be calculated from the available manual inspection data for any pipeline.

This process for determining the expected worst corrosion for a pipeline is quick and automatic. It allows the assets to use their inspection data to easily update their understanding of pipeline condition as new inspections become available. This guides decisions for further inspection needs or possible repair requirements. The process is readily updated as new ILI's provide additional information that can be used to improve the extreme value distribution selection algorithm.

This paper builds on technology that is described in SPE 128697 (Ziegel et al 2009), which demonstrated that the adequacy of the number of inspections for a pipeline could be assessed by using the available ILI data to establish sampling distributions as the basis for sample size requirements for manual inspections. The two technologies together turn a lot of pipeline inspections into a direct awareness of the statistical assuredness of the current understanding of pipeline condition. The additional information improves the ability of inspection and repair functions to ensure that oil remains in pipelines. Loss of containment can result in millions of dollars of lost profits in a single day and have a very negative impact on the reputation of the operator.

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