Open AccessA review on oil and gas pipelines corrosion growth rate modelling incorporating artificial intelligence approach
Author(s) -
Ahmed M. M. Nasser,
O.A. Montasir,
Noor Amila Wan Abdullah Zawawi,
Shamsan Alsubal
Publication year - 2020
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/476/1/012024
Subject(s) - pipeline transport , corrosion , integrity management , probabilistic logic , artificial neural network , magnetic flux leakage , fuzzy logic , engineering , pipeline (software) , computer science , petroleum engineering , artificial intelligence , materials science , mechanical engineering , metallurgy , magnet
One of the necessities of an effective oil and gas pipeline safety Management Plan (SMP) is the establishment of safe and efficient risk assessment strategy for pipelines where the significant danger is corrosion. Corrosion growth is related to several factors involving pipe material, pipe condition, and defect geometrical imperfection. Thus, the assurance of a proper corrosion assessment requires the prediction and evaluation of corrosion growth rates. The prediction of corrosion growth rate precisely, would minimize the cost of pipelines maintenance through the determination of the deteriorated pipeline segments. In line inspection (ILI) has been used to detect the pipelines corrosion, also the corrosion can be detected by other inspection tools such as Magnetic flux leakage (MFL) and Ultrasonic tool (UT). However, there are numerous models have been utilized to anticipate the corrosion growth rate such as deterministic and probabilistic models. Recently, there are conducted researches on the application of artificial intelligence in predicting corrosion growth rate for oil and gas pipelines such as artificial neural network (ANN) and fuzzy logic (FL). This paper aims to provide a comprehensive comparison between the conventional methods, i.e. deterministic and probabilistic and artificial intelligence methods, i.e. Artificial neural network (ANN) and fuzzy logic (FL) in the prediction of corrosion growth rate of oil and gas pipelines. This review would be helpful to pipelines operators to understand the effectiveness of artificial intelligence approach compared to conventional methods in corrosion growth rate modelling.