Session: 03-02-01 Machine Learning , AI and Applications 1.1

Paper Number: 133928

133928 - Dent Safe Excavation Pressure and Fracture Reliability Assessment With Machine Learning 

Abstract: 

Dents can potentially cause cracking or pipeline leaks posing a threat to pipeline integrity. Engineering critical assessment of dents interacting with cracks plays an important role in the risk and integrity management of pipelines. It is not an easy task to conduct fracture assessment on a dent-crack defect due to the complexity of the stress field in the dent area. The failure assessment diagram (FAD) method has been widely used to conduct elastic-plastic fracture mechanics analysis of structural components. The FAD approach can be used to conduct fracture assessment for an arbitrary through-wall thickness stress distribution in a pipe with cracks. Finite element simulations are normally required to extract the stress distributions for a pipeline dent. However, the process is computationally demanding and tedious.         

A machine learning-based methodology has been developed to improve the efficiency of the process of dent fracture assessment. The machine learning algorithms were trained using a dataset built from finite element simulations of dents. Through-wall thickness stress distributions at multiple critical locations with potential cracks in the dents were extracted for varied dent geometries at multiple pressure levels. The extracted stress distributions were used to calculate stress intensity factors (SIFs) for a wide range of crack dimensions based on the SIF solutions obtained from API 579. The output variable of the machine learning model was defined as the ratios of SIF with and without dents. The inputs to the machine learning algorithms to train on include pipe and dent geometries, crack dimensions and pipe internal pressure. The model performance was evaluated through cross-validation. It was shown that the trained machine learning model was able to effectively predict SIFs for dent-crack defects with varied dent geometries. The model was also shown to be robust when used to conduct Monte-Carlo simulations as part of a reliability assessment to develop a distribution of SIF accounting for uncertainties on the model input variables.             

Dent fracture assessment with the trained machine learning model was employed in the determination of safe excavation pressure (SEP) based on a reliability analysis. The fracture assessment was conducted using the API 579 FAD approach. The effectiveness of the FAD method on the prediction of burst pressure limits for dent-crack defects was validated with full-scale tests. It was then shown that the machine learning model-based approach was effective in conducting reliability-based fracture assessment on dent-crack defects with the FAD method to determine SEPs corresponding to given reliability targets. The efficiency on the process of SEP determination has been significantly improved compared with the surrogate modeling approach using stress distributions extracted from the finite element simulations.        

Presenting Author: Huang Tang Integrity Risk Assessment Specialist

Presenting Author Biography: Huang has over 15 years of experience in oil and gas industry. He joined Enbridge in 2021 as a pipeline integrity risk assessment specialist. His work has been focused on dent strain and fracture assessment with machine learning and FEA simulations and MAOP reconfirmation. Before joining Enbridge, he was with ExxonMobil and has over 13 years of experience with the company. At ExxonMobil, he worked heavily on the research and development of strain-based pipeline design technology, fitness for service assessment and hydrogen embrittlement and sour service. Huang Tang received his bachelor’s degree in mechanical engineering from Tsinghua University and his Ph.D. degree in civil engineering from Carnegie Mellon University. He conducted postdoctoral research at MIT and Northwestern University with a focus on advanced modeling of mechanical behavior of materials.

Authors:

Huang Tang Enbridge GTM
Martin Di Blasi enbridge
Chike Okoloekwe Enbridge
Nader Yoosef-Ghodsi Enbridge