Advanced intelligence frameworks for predicting maximum pitting corrosion depth in oil and gas pipelines

Mohamed El Amine Ben Seghier, Behrooz Keshtegar, Mohammed Taleb-Berrouane, Rouzbeh Abbassi, Nguyen-Thoi Trung

Research output: Contribution to journalArticle

Abstract

The main objective of this paper is to develop accurate novel frameworks for the estimation of the maximum pitting corrosion depth in oil and gas pipelines based on data-driven techniques. Thus, different advanced approaches using Artificial Intelligence (AI) models were applied, including Artificial Neural Network (ANN), M5 Tree (M5Tree), Multivariate Adaptive Regression Splines (MARS), Locally Weighted Polynomials (LWP), Kriging (KR), and Extreme Learning Machines (ELM). Additionally, a total of 259 measurement samples of maximum pitting corrosion depth for pipelines located in different environments were extracted from the literature and used for developing the AI-models in terms of training and testing.Furthermore, an investigation was carried out on the relationship between the maximum pitting depths and several combinations of probable factors that induce the pitting growth process such as the pipeline age, and the surrounding environmental properties. The results of the proposed AI-frameworks were compared using various criteria. Thus, statistical, uncertainty and external validation analyses were utilized to compare the efficiency and accuracy of the proposed AI-models and to investigate the main contributing factors for accurate predictions of the maximum pitting depth in the oil and gas pipeline.
Original languageEnglish
Pages (from-to)818-833
Number of pages16
JournalProcess Safety and Environmental Protection
Volume147
DOIs
Publication statusPublished - Mar 2021

Keywords

  • Oil and gas pipelines
  • Pitting corrosion
  • Maximum depth
  • Artificial Intelligence (AI)
  • Global performance indicator (GPI)
  • External validationa
  • External validation

Fingerprint Dive into the research topics of 'Advanced intelligence frameworks for predicting maximum pitting corrosion depth in oil and gas pipelines'. Together they form a unique fingerprint.

Cite this