# Bayesian Survival Analysis Model for Girth Weld Failure Prediction  
[PERSON]  
1 Pipeline Integrity Management Center, PetroChina Pipeline Company, Langfang 065000, China; [EMAIL_ADDRESS]
2 College of Pipeline and Civil Engineering, China University of Petroleum, Qingdao 266580, China
3 The Erosion/Corrosion Research Center, The University of Tulsa, Tulsa, OK 74104, USA
4 School of Materials Science and Engineering, Tianjin University, Tianjin 300072, China; [EMAIL_ADDRESS]  
November 8, 2021  
technology, magnetic flux leakage (MFL) techniques are still considered to be the most cost-effective and easy-to-implement techniques among girth-well defect-detection methods [9; 10]. However, the irregular girth-well morphology makes it difficult to accurately identify and quantify the girth-well defects in a short detection window. At present, there are no specific precision requirements for MFL inspection for girth-well defects. The decision for girth-well repair plans in accordance with the standards of the Fitness for Service (FFS) and Engineering Critical Assessment (ECA)methods [11; 12; 13] based on MFL inspections alone may significantly increase the operating costs, because of the possibility of large deviations in the data. Decisions based on this type of data cannot completely eliminate the risk of girth-well failure. Furthermore, the factors influencing the failure of girth welds are relatively complex and still difficult to quantify and analyze and include welding defects, weld geometry, and loading, which is also affected by complex external factors, such as soil displacement, earthquakes, floods.  
Traditional managers are accustomed to using general knowledge, limited experience, and limited data to determine the possible risk factors and locations of leaks in pipelines. However, the limitation of this method has been to use the data collected from other pipelines to analyze the data of a specific pipeline by analogy. Additionally, insufficient data regarding the pipe and the surrounding environment lead to the incorrect evaluation of the results. After decades of development, this model has improved and become a relatively more mature technology, with quite remarkable effects [14; 15]. Many experts continue to revise evaluation models and algorithms in this area, so that the accuracy of risk prediction is continuously improved under limited cognitive conditions [16; 17]. However, recent pipeline accidents show that there is an urgent need for research on how to use the information to identify and evaluate the failure of girth welds and on to how convert the information into practical aspects of integrity management to improve the safety of pipeline operation. Therefore, current research has tended to focus on new prediction methods in substitution of the traditional risk-assessment model. Although the current information or data have certain problems such as accuracy, they are enough to help to improve the evaluation of the safety status of the pipeline.  
Previous research focused on discussing the knowledge of Pipeline Big Data (PBD) and what will change in the risk model [18]. With the development of technology, part of the dataset of the PetroChina pipe has become systematic and comprehensive, helping to almost fully understand the risk factors of the pipeline. This has permitted to change from \"black-box\" identification and evaluation of the risk factors to \"white-box\" identification and full control of the operators. This paper is an attempt to set up a framework method of girth-well prediction with the Bayesian survival-analysis algorithm based on data t, \"tree-type\" accident-cause theory, and anti-fragile concept to avoid the method of simple data analysis or the traditional risk analysis [19; 20]. This framework is aimed to improve the tools of girth-well failure prediction to reduce accidents and prevent disasters similar to the San Bruno pipeline rupture.