Research on corrosion defect identification and risk assessment of well control equipment based on a machine learning algorithm
Title
Research on corrosion defect identification and risk assessment of well control equipment based on a machine learning algorithm
Subject
K-means++
KNN
Machine learning
Pulse magnetic flux leakage testing
Risk assessment
Description
In recent years, the risk assessment of well control equipment has faced some problems, such as shallow defect detection depth, large identification error of corrosion defect type, inaccurate equipment corrosion assessment, and so on. To solve the above problems, a corrosion defect classification and identification model based on an improved K Nearest Neighbor algorithm (KNN) is established for the well control pipeline in well control equipment. Firstly, the Pulsed Magnetic Flux Leakage (PMFL) sensor is used to detect the pipeline defects, and then the collected data are denoised. Then, the corrosion type identification model of well control pipeline based on K-means++ and KNN is established. Finally, the corrosion risk of well control pipeline is evaluated according to the type of corrosion output from the identification model. The experimental results show that the improved algorithm has high accuracy in identifying the corrosion type of well control pipeline, and the calculation speed is better than other algorithms described in this paper.
Creator
Zhang, He
Cao, Jiangna
Liang, Haibo
Cheng, Gang
Publisher
Petroleum
Date
2022
Type
journalArticle
Identifier
2405-6561
10.1016/j.petlm.2022.07.003
Collection
Citation
Zhang, He et al., “Research on corrosion defect identification and risk assessment of well control equipment based on a machine learning algorithm,” Lamar University Midstream Center Research, accessed May 13, 2024, https://lumc.omeka.net/items/show/27515.