The key to estimating the probability of detection (POD) of an inline inspection tool is to count existing defects that have not been detected by the tool. In an ideal experiment, the number of defects in the pipeline is known before running the tool and the rate of detection is calculated by comparing the number of defects detected by the inline tool with the total number of defects in the pipeline. In reality, the inline inspection is often the first method of identifying defects. Undetected defects are found during excavation only if they are near defects called by the inline tool.
This paper describes a new model for probability of detection based on a combination of excavation data and the vendor claim. It addresses the limitations of existing models by assuming undetected defects can occur anywhere in the tool run. This is accomplished by modeling the rate of undetected defects using a Poisson distribution.
Its main advantages are a more accurate representation of the probability of detection that considers all the available data, the flexibility to update the model with additional data sets as they become available and ability to quantify changes in the uncertainty of POD as additional data is uncovered through excavations.
Overall, a more accurate and defensible process for determining probability of detection is proposed which can be used for managing pipeline integrity and risk.
