Systemic manufacturing defects in select vintage pipe pose challenges when assessing the integrity of pipeline systems comprised of such pipe. The common manufacturing technology and quality control practices in place at the time of manufacturing left some vintage line pipe prone to imperfections which could remain even after passing pressure tests in the mill or after construction. The lack of complete and reliable manufacturing records for some vintage line pipe limits granularity and adds integrity assessment uncertainties. Up until 1984, the United States Department of Transportation (USDOT) Pipeline and Hazardous Materials Safety Administration (PHMSA) required operators to report incidents related to failed pressure tests for all pipelines at the time of installation. Performance with respect to the manufacturer and year of manufacture can therefore be extracted from these reported incidents. These performance records are essential when re-establishing the MAOP (or MOP) and confirming the fitness for service of older pipelines. The pressure test failure performance in the early incident records provides insight into pipeline integrity prioritization and mitigation activities for managing pipeline safety based on pipe manufacturer, production date and seam type.
When estimating pipeline burst pressure, one of the prevalent sources of uncertainty that needs to be factored into the calculation 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 many ILI vendors have feature specific error bounds depending on the morphologies of the corrosion, 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. At the most, the error is reported by classes based on corrosion morphologies specified by Pipeline Operators Forum. For example, a commonly reported corrosion depth sizing specification is ±10% of pipe wall thickness at 80% confidence for the General type of corrosion. 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, the 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 of such an approach would be a feature specific error tolerance, this could be that the estimated depth of a feature is 36%wt 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, 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.
Since the 1970s, the United States Department of Transportation (USDOT) Pipeline and Hazardous Materials Safety Administration (PHMSA) has collected and published pipeline failure incident data. Operators are required to report pipeline incidents and provide the apparent cause of failures. PHMSA and ASME (B31.8S for gas and B31.4 for liquids) identify and group these failures into nine broad categories and sub-classify them into three clusters by their time-based behavior. Technical advancements in pipe manufacturing, fabrication, construction, operation, inspection, monitoring, maintenance, rehabilitation and regulation have resulted in a decrease in incidents for many of these failure causes. This paper presents a statistical trending analysis of the failure incidents for each of the nine threats. The multi-year trending of these incident metrics over the last 40+ years will be demonstrated.
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