Predicting Stress Corrosion Cracking: A Bayesian Network Approach for Duplex Stainless Steels

Predicting Stress Corrosion Cracking: A Bayesian Network Approach for Duplex Stainless Steels

Presented at the 2024 BayesiaLab Conference in Cincinnati on April 12, 2024.


Duplex stainless steel (DSS) alloys, recognised for their high mechanical strength and corrosion resistance, are increasingly utilised in the oil and gas industry to mitigate critical degradation risks in downhole environments. Elevated pressures, temperatures, and corrosive agents such as chlorides, carbon dioxide, and hydrogen sulphide characterise these settings. Collectively, these factors contribute to various environmentally assisted cracking mechanisms, predominantly stress corrosion cracking (SCC). This corrosion phenomenon significantly threatens production systems, potentially causing premature failures of metallic materials due to the synergistic effects of tensile stresses and corrosive media. Despite the growing adoption of DSS alloys, their performance in oil and gas applications remains inadequately understood within existing standards, rendering the operational boundaries of DSS perceived as overly conservative. While comprehensive research has explored DSS's resistance to SCC, the reliable assessment of SCC risks in the field remains a significant challenge. Consequently, there is a need for a framework to evaluate, with reasonable certainty, the viability of DSS applications in production systems. We address these limitations by introducing a data-centric approach through Bayesian networks (BNs) for assessing the SCC risks of DSS in downhole environments. We developed this BN model by combining various information sources, including industry standards, technical guidelines, and scientific papers. We used advanced pre-processing techniques, such as data imputation and synthetic minority oversampling, to prepare the dataset adequately. Furthermore, the BN model's structure and predictive accuracy were also compared with other modelling methods, such as XGBoost and SHAP analysis, which provide additional insights into the causality of SCC. More importantly, our BN model demonstrates that the SCC resistance of DSS alloys can comfortably exceed the operational threshold established in standards, currently within 0.02 – 0.2 bar of the partial pressure of hydrogen sulphide.


Abraham Rojas Zuniga1^1

Sam Bakhtiari1^1

Ke Wang1^1

Chirs Aldrich2^2

Victor M. Calo3^3

Mariano Iannuzzi1^1

1^1Curtin Corrosion Centre, Faculty of Science and Engineering, Curtin University.

2^2Western Australian School of Mines: Minerals, Energy and Chemical Engineering, Faculty of Science and Engineering, Curtin University.

3^3Computing and Mathematical Sciences, Faculty of Science and Engineering, Curtin University.

About the Presenter

Abraham Rojas Zuniga

As a petroleum engineer with five years of experience, I have advanced my academic career with a Master's degree (M.Phil.) in Oil and Gas Engineering from the University of Western Australia. As a Ph.D. candidate at Curtin University, my research focuses on chemical engineering and artificial intelligence to improve our understanding of corrosion phenomena in hydrocarbon industry alloys. I am keenly interested in applying simulation techniques, ranging from deterministic models to data-driven methods, to investigate material science phenomena and enhance risk assessment strategies.

Abraham Rojas Zuniga

Previous Conference Presentations


For North America

Bayesia USA

4235 Hillsboro Pike
Suite 300-688
Nashville, TN 37215, USA

+1 888-386-8383

Head Office

Bayesia S.A.S.

Parc Ceres, Batiment N 21
rue Ferdinand Buisson
53810 Change, France

For Asia/Pacific

Bayesia Singapore

1 Fusionopolis Place
#03-20 Galaxis
Singapore 138522

Copyright © 2024 Bayesia S.A.S., Bayesia USA, LLC, and Bayesia Singapore Pte. Ltd. All Rights Reserved.