Girth weld cracking of long-distance oil and gas pipelines yields substantial harm to pipeline safety and may cause serious accidents. As of today, non-destructive testing has been one of the most common methods for predicting potential faults and ensuring safe operation. Classical pipeline non-destructive testing methods include magnetic flux leakage testing and the use of ultrasonic testing by electromagnetic acoustic transducers. However, they are incapable of identifying the defects in complex surfaces like girth welds. Magnetic flux leakage testing exhibits poor anti-interference abilities and low space resolution. Ultrasonic testing by electromagnetic acoustic transducers suffer from low conversion efficiency and poor signal quality. In order to overcome the disadvantages of conventional pipeline non-destructive testing methods, we propose an embedded eddy current testing system by leveraging image processing and neural networks. Hough transform and the contour extraction technique are employed to extract the characteristic features from the two-dimensional (2D) eddy current impedance image. Experiment results show that the system can effectively identify the girth weld defects, featuring an accuracy of up to 92%. The low power consumption and compactness of the proposed system makes it a great candidate for pipeline inner inspection.

The formation and development of a dataset for pipeline systems have affected the management and decision-making of pipeline operators. The dataset, combined with a proposed theoretical analysis method, can provide significant improvement for the safe and economic operation of pipelines. On the basis of the pipeline data and its essential impact on pipeline risk assessment, the authors propose for the first time the Staged Bayesian failure model for girth welds of a pipeline, using the “tree-type” accident theory and Bayesian survival analysis method. This model of girth welds is consistent with the distribution of Kaplan–Meier functions and can predict the influence of different factors on the survival probability of girth welds. These new research results can lay the technical foundation for the failure analysis of pipeline girth welds.

Globally, more and more attention has been paid to the integrity of Girth Welds (GW) of oil and gas pipelines due to their failures with high consequences. A primary concern is that defects originate during field construction but over time may be subject to external loads due to earth movement. GW defects in newly built pipelines are also assumed to exist but would be much smaller in size, and more difficult to detect, which motivated the investigation into minimum defect detection capabilities of the inspection technologies. This study presents the evaluation results of UltraScan™ Circumferential Crack-Like Detection (USCCD) technology for oil pipeline GW inspection, based upon the pull test and in field data from Inline Inspection (ILI) of pipeline by PetroChina Pipeline Company (PPC) using GE PII (General Electric Company, Pipeline Integrity Inspection) 32” UltraScan™ CCD Tool. The performance of USCCD is given according to the ILI data, pull test results and dig NDE (Non-Destructive Examination). It can be concluded that crack-like defects with clear edges can be detected during ultrasonic propagation; however, the irregular shape of weld makes the inspection more difficult. It is still a challenge to identify the type of defects, and depth sizing can only be classified not quantified, which would require more excavations. However, this technology is feasible for the alternative technology of GW defect inspection.

Due to the wide application of flux-cored arc welds (FCAW) susceptible to significant scatter in weld and Heat Affected Zone (HAZ) fracture toughness, there is an interest in methods for evaluating the reliability of welds containing defects. The mechanical properties of the FCAW girth weld of an X80 pipeline are tested and then analyzed. By obtaining fracture toughness results from a statistically significant number of SENB specimens, with notches positioned in different HAZ locations, the effect of variation within the results can be evaluated. The results of the fracture toughness tests were analyzed using statistical methods, to compare both the difference in behavior between HAZ microstructures and the variation when a similar microstructure has been sampled. The range of different characteristic toughness values was analyzed using a postulated ECA case to illustrate the sensitivity of the results to how toughness is defined. The analyses supported recommendations to be made on the optimum approach to characterizing HAZ fracture toughness for reliable pipeline assessments in multi-pass girth welds with complex HAZ microstructure distributions.

Above ground indirect detections and random excavations that have applied the past years for buried long distance oil and gas pipelines can only identify some damaged coating locations. Hence, large number of field joint coating (FJC) failures happen unconsciously until they lead to failures of the pipelines. Based on the analysis of magnetic flux leakage (MFL) in-line inspection (ILI) signals, combined with the statistical results of 414 excavations from two different pipeline sections, a new method to identify the failed FJC is established. Though it can only identify FJC failures when there are signs of corrosion on pipe body, it is much more efficient and cost-saving. The concluded identification rule still needs more validations and improvements to be more applicable and accuracy.

The fault tree of the pipeline was studied by investigating the failure modes and statistics of pipeline accidents of PetroChina. After a quantitative analysis of the impact factors for different failure modes, a Quantitative method for calculating the failure possibility of pipeline was established. The result was used to improve the most widespread pipeline risk assessment method - Kent scoring method. It can give comparable quantified description of the pipeline risk under current operation condition, and can give further support for decision-making of integrity management.