Dates: January 29 – February 1, 2018
Location: George R. Brown Convention Cnt. | Houston, Texas
Stress Engineering Services, Inc. (SES) assisted a pipeline operator with the analysis of an in-service pipeline leak identified in a location where an adjacent pipeline road-crossing had been replaced. ILI data indicated that the leak coincided with a top-of-the-pipe dent. After receiving the pipe sample, SES performed a combination of metallurgical and numerical analysis to determine the likely causes of the observed deformation and the subsequent leak. SES visually examined the pipe and found multiple wrinkles/dents spanning approximately two-thirds of the pipe circumference. Additionally, four cracks were identified (two through-wall and two that contained internal and external surface breaking cracks that did not extend through-wall) in the peaks and valleys of the deformation. All of the observed cracks extended via shear fracture at a 45° orientation to the pipe surface and appeared to be ductile overload based on scanning electron microscopy. Numerical analysis, using FEA modeling, indicated that the areas of cracking correlated with the areas of high stress. This analysis demonstrated that an accumulation of strain (ratcheting) occurred in the subject pipeline due to repeated internal pressure cycles. Based on the results of metallurgical and numerical analyses, SES concluded that the complex geometry of the buckled region was the primary reason for the ratcheting behavior and failure. The interaction between the adjacent dents and wrinkles created a situation where the anomaly region alternated between tensile and compressive yielding during internal pressure loading and unloading.
The operation of today’s pipelines is critically dependent upon the data provided by in-line inspection (ILI) tools. In order to effectively use the data from ILI tools to make informed integrity management decisions, pipeline operators must understand both the capabilities and limitations of these tools. Validation of ILI tools often consists of inspecting known flaws in a testing environment and comparing the results to the tool’s published specification sheet. However, these results are susceptible to testing and data processing biases since they do not represent the actual conditions of an ILI run. The purpose of this study was to eliminate these biases by developing test spools that were installed in-situ during actual ILI runs to verify the tool specifications from various vendors. Over 120 crack-like anomalies were installed in the test spools to ensure a large enough sample size for the results to be statistically significant. The results from initial ILI tool runs highlighted areas for improvement in external versus internal anomaly discrimination, depth sizing near the tool saturation point, and detection of complex features (e.g. stacked flaws). The results also confirmed performance of the tool at or above its published specification for anomaly sizing.
While NDE data is typically more accurate than ILI, only a limited number of excavations or digs can be practically performed. Two questions regarding the use of the ILI and NDE data are addressed in this paper: (1) how to validate the ILI tool against NDE results to assess the performance of the ILI tool in field conditions, and (2) how many NDE digs should be performed such that a statistically significant number of samples are available for validation to be meaningful. Data from two pipelines is analyzed in this paper and demonstrates that informed integrity decisions can be made by utilizing multiple statistical methods, since different methods can give slightly different results. The first case study uses data from ILI scan of a pipeline (referred to as Pipeline A) with an ultrasonic crack detection tool that resulted in more than 40,000 gradable crack and crack-type features. More than 100 excavations were performed to measure features using NDE techniques. In the second case study, data from an ultrasonic ILI tool for detecting metal loss due to corrosion for another pipeline (referred to as Pipeline B) was used. The tool detected more than 200 internal wall loss features. The goal was to estimate the statistically significant number of NDE digs that will be required for the ILI tool performance assessment with NDE data to be valid.