The goal of this compilation is to educate the reader through experiences and observations from real-world applications and to provide tools for the identification and remediation of pipeline corrosion issues before failure. Pipelines have been used to transport fuels since the 19th century. While there is no clear consensus of the total number of pipeline miles in use throughout the world, most agree that the U.S. has over 2.5 million mi of energy pipelines. This book provide tools for the identification and remediation of pipeline corrosion issues before failure.
2018 NACE e-book
Estimating corrosion growth rates for underground pipelines is a challenging problem. There are confounding variables with complex interaction effects that may result in unexpected outcomes. For instance, the relationship between soil conditions and AC interference is highly non-linear and challenging to model. This work expands upon prior work using a suite of machine learning tools to estimate corrosion rates. However, instead of estimating a single corrosion growth rate for a single girth weld address (GWA), this work estimates a distribution of potential corrosion growth rates. Modeling distributions provide a more effective risk-measurement framework, especially concerning high volatility or areas of severe tail risk.
This work relies heavily on machine learning and geospatial tools - particularly artificial neural networks and gradient boosted trees to estimate the corrosion rates and non-linear processes. Building upon prior work using data from a North American Operator, the models in this paper use additional variables from recent research in AC interference and microbiologically influenced corrosion to construct a higher accuracy and distribution-based model of pipeline corrosion risk.