Uncertainty and measurement error need to be understood to effectively manage the integrity of a pipeline. In-line inspection data are inherently uncertain, which means the reported dimensions of an anomaly may be higher or lower than the actual values.  A pipeline operator will consider specified tool tolerance to ensure conservative assumptions are applied during anomaly dig selection. Actionable anomaly depth criteria, burst pressure calculations, and corrosion growth rate estimates are examples where potential for data uncertainty and bias need to be factored.

One of the prevalent sources of uncertainty that needs to be factored is the model error in the estimation of feature depth and length from the in-line inspection tool. Due to modeling technique limitation, as of today, this error can only be reported to operators as an overall error known as the ILI tool tolerance which is usually obtained from samples of excavation data or pull test data.  For example, a commonly reported corrosion depth sizing specification is ±10% of pipe wall thickness at 80% confidence. \[HD1\] This can be interpreted as that the error of each reported depth estimations is assumed to fall in a normal distribution with a mean equal to 0 and standard deviation equal to 7.8% of wall thickness. The shape of the distribution, mean and standard deviation will then be used as constants to factor in the burst pressure calculation.

However, these factors are never constant for a sample of defects in reality. In fact, they ought to be variables on an individual feature basis. An example for this a feature specific error tolerance could be the estimated depth of a feature is 36% w.t. in an interval of \[30%, 48%\] of wall thickness with 80% confidence. This is believed to greatly reduce the level of uncertainty when it comes to failure pressure estimation or other type of pipeline risk assessment. The advancement in Machine Learning today, deep learning with deep neural networks in particular, allows feature-specific error tolerance to be obtained after analyzing visual imagery of MFL signal. In this paper we will describe a novel approach to predict the size of metal loss defects and more importantly the distribution associated with each prediction. We will then discuss the benefits of this approach has with respect to risk assessment such as failure pressure estimation.

 \[HD1\]Based on the Pipeline Operator Forum document. This is a standard sizing tolerance specification most commonly adopted by ILI technologies.


A Feature-Specific Probabilistic Assessment of Pipeline Defect Size From Ili Mfl Signal Using Convolutional Neural Network

Division / Sponsors: 
Pipeline Systems Division

Paper Proceedings: https://asmedigitalcollection.asme.org/IPC/IPC2020/volume/84447

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