Decision making through a Bayesian network for a pipeline in design
Francois Ayello A , Guanlan Liu A and Jiana Zhang B CA DNV GL Columbus, 5777 Frantz Road, Dublin, OH 43017, USA.
B DNV GL Australia, Level 2, 89 St Georges Terrace, Perth, WA 6000, Australia.
C Corresponding author. Email: Jiana.Zhang@dnvgl.com
The APPEA Journal 59(1) 156-165 https://doi.org/10.1071/AJ18048
Submitted: 7 December 2018 Accepted: 8 February 2019 Published: 17 June 2019
Abstract
Decision making for investing in a new pipeline project can be a long and costly process. This is usually due to the uncertainty and missing information regarding the interactions of parameters (e.g. brine chemistry, flow conditions or scale deposition) during internal corrosion assessment. In addition, these interactions result in multiple forms of internal corrosion threats (i.e. uniform corrosion, localised corrosion, erosion-corrosion and microbiologically influenced corrosion). Currently, there are no corrosion models in the market that consider all the different corrosion threats, and the predicted corrosion rates are normally conservative, leading to high overall project cost from the usage of higher grade construction material or strict maintenance regime. To predict a much more accurate internal corrosion rate with consideration of all possible corrosion mechanisms in a pipeline, a Bayesian network (BN) model was created that identifies and quantifies the causal relationships between parameters influencing internal corrosion. The model had previously proven its accuracy in predicting the internal condition of operational pipelines where explicit knowledge is available. However, the model has never been applied for a pipeline in design stage, where the design is based on tacit knowledge. In this study, to evaluate the applicability of this BN model on the pipeline at design stage, an offshore pipeline was assessed for internal corrosion.
Keywords: corrosion, prediction.
Dr Francis Ayello is a Principal Engineer in DNV GL’s risk department in Dublin, Ohio. He has more than 10 years of experience in the oil and gas industry working primarily on developing corrosion and flow models. He obtained a Ph.D. from Ohio University in 2009 and a Master’s degree from Pau University (France) in 2004, both in chemical engineering. He has published over 50 papers in journals and conferences. His main technical interests involve the development of novel mathematical methods to quantify risks of aging systems. |
Guanlan Liu works as an Engineer in DNV GL, focusing on pipeline risk assessment. He holds a Ph.D. in Chemical Engineering from Texas A&M University and B.S./M.S. degrees from Tsinghua University. Prior to his current position in DNV GL, Guanlan worked as a Postdoctoral Researcher in Mary Kay O’Connor Process Safety Center. |
Jiana Peska (Zhang), is an engineer with 6 years of experience working in the oil and gas industry, mainly in the field of corrosion and coating. She has been involved in various Australian projects where corrosion verification and advisory services are required. |
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