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Predictive Analytics and Machine Learning in Integrated External Corrosion Management

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Managing external corrosion, especially for underground assets, is a significant challenge dating back to the first underground pipeline in 1865. The very first issue of the journal, CORROSION, featured a headline story on this subject. This subject is fundamental for corrosion engineers and pipeline operators.

Product Number: 51323-19393-SG
Author: Thomas Hayden, Joseph Mazzella, Keith Parker, Christophe Baete
Publication Date: 2023
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Integrated External Corrosion Management (IECM) is a novel framework developed for pipeline operators to model, identify, and optimize external corrosion risk and costs using a data-driven approach. Over the last decade, Machine Learning (ML) has transformed industries from consumer technology to product design to industrial systems. In corrosion, the Association for Materials Protection and Performance (AMPP) has added a symposium for specialists designing and optimizing machine learning algorithms detection and management. This work is not about a specific algorithm or technology set. Instead, this work presents a framework for incorporating the output of a predictive algorithm with an IECM framework. This work considers the interplay between in-line inspection (ILI), direct assessment, close interval surveys, and mechanistic modeling. Lastly, this work describes an external corrosion management system that is fully "observable", an environment where the state of any component in a pipeline system can either be directly observed or inferred in near real-time.

Integrated External Corrosion Management (IECM) is a novel framework developed for pipeline operators to model, identify, and optimize external corrosion risk and costs using a data-driven approach. Over the last decade, Machine Learning (ML) has transformed industries from consumer technology to product design to industrial systems. In corrosion, the Association for Materials Protection and Performance (AMPP) has added a symposium for specialists designing and optimizing machine learning algorithms detection and management. This work is not about a specific algorithm or technology set. Instead, this work presents a framework for incorporating the output of a predictive algorithm with an IECM framework. This work considers the interplay between in-line inspection (ILI), direct assessment, close interval surveys, and mechanistic modeling. Lastly, this work describes an external corrosion management system that is fully "observable", an environment where the state of any component in a pipeline system can either be directly observed or inferred in near real-time.

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How Not to Do Machine Learning for Corrosion Prediction

Product Number: 51323-19310-SG
Author: Thomas Hayden, Joseph Mazzella, Len Krissa, Paul Murray
Publication Date: 2023
$20.00

The intention of this work is to pose epistemic questions about corrosion measurement, statistical inference, and the role of machine learning in predicting corrosion growth. The audience of this work is practitioners implementing inferential algorithms or tools for corrosion prediction. In this work, an algorithm consists of a process for estimating the presence and severity of corrosion.
Picture for Case Study: Integrated External Corrosion Management for A 12-Inch Crude Oil Pipeline
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Case Study: Integrated External Corrosion Management for A 12-Inch Crude Oil Pipeline

Product Number: 51323-18970-SG
Author: Keith Parker, Trey Johnston, Christophe Baeté, Michael Snow
Publication Date: 2023
$20.00

The Enbridge External Corrosion Prevention team (ECP) is developing an Integrated External Corrosion Management (IECM) process by which management of external corrosion control systems can be optimized while
maintaining asset integrity and safety. IECM is intended to provide a methodology by which operators can move from a traditional reactive approach to a state-of-the-art proactive strategy commensurate with industry expertise and technology. Here we discuss a case study intended to demonstrate and assess the outcomes of IECM. The subject is a 12-inch diameter crude oil pipeline located in North America. Results of the process reveal opportunities for improvement and efficiencies in operation and maintenance (O&M).
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Estimating Corrosion Rates for Underground Pipelines: A Machine Learning Approach

Product Number: 51319-13456-SG
Author: Joseph Mazzella, Len Krissa, Thomas Hayden, Haralampos Tsaprailis
Publication Date: 2019
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Estimating corrosion growth rate for underground pipelines is a non-linear multivariate problem. There are many potential confounding variables such as soil parameters cathodic protection AC/DC interference seasonal / climate conditions and proximity to unique geographic features such as wetlands or polluted environments. The work presented provides an approach for estimating underground corrosion growth rates using a dataset of observations from a North American pipeline operator. Extensive geospatial data is utilized that has been obtained from public and private sources and extrapolated using Inverse distance weighted (IDW) interpolation. This work presents a model using IDW to estimate parameters involving soil interference geography and climate factors for any location in North America.Using this data this work then presents several different machine learning approaches including Generalized Linear Models eXtreme Boosted Trees and Neural Networks. All three provide an accurate estimation for corrosion growth rates for an underground asset at any latitude and longitude pair in North America. Each method comes with potential benefits and pitfalls specifically; trade-offs between model accuracy and transparency. This work presents a framework for comparing geo-spatial and machine learning estimates. Findings and a framework are provided for owners to assess how to think machine learning on their own assets.
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