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Optimizing Pipeline AC Threat Assessments using Computational Aided Engineering (CAE) Modelling

Picture for Optimizing Pipeline AC Threat Assessments using Computational Aided Engineering (CAE) Modelling
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There are several well documented reports and standards that detail what information is required to assist with an AC threat assessment. The Association for Materials Protection and Performance (AMPP) also details specific requirements and recommendations in terms of AC and the type of data and information required, in the NACE standard SP0177-2019, “Mitigation of Alternating Current and Lightning Effects on Metallic Structures and Corrosion Control Systems”. The documents and reports which address AC threat assessment, and which are available today, are all premised upon best engineering practices.

Product Number: 51323-19219-SG
Author: Christophe Baeté, Dr Olga Dolgikh
Publication Date: 2023
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$20.00
$20.00

AC threat assessments on pipelines are typically covered under various specifications, and best practices, such as NACE SP0177, EN 50443 and the INGAA FINAL Report No. 2015-04. The theories and philosophies are all based upon sound engineering and scientific principles but require a significant amount of Subject Matter Expert (SME) time. The more complex the collocations, the slower the process and/or the higher the costs. With complex and large networks, the asset owner is often overwhelmed.


An advanced approach has been developed, tested, and implemented on more than 66,000 miles of pipelines. During testing, previously qualified and quantified AC threats, investigated using more comprehensive site and engineering technologies were used as a comparison. This paper addresses how the AC threat assessment can be used in conjunction with readily available pipeline operator data, in-house expertise, as well as other publicly available source data, to compute the level of the AC threat and determine critical regions depending on the operational mode of AC threat, quickly, effectively and accurately in an extremely cost effective manner, and in line with the pending PHMSA Mega Rule part 2 (RIN2).

AC threat assessments on pipelines are typically covered under various specifications, and best practices, such as NACE SP0177, EN 50443 and the INGAA FINAL Report No. 2015-04. The theories and philosophies are all based upon sound engineering and scientific principles but require a significant amount of Subject Matter Expert (SME) time. The more complex the collocations, the slower the process and/or the higher the costs. With complex and large networks, the asset owner is often overwhelmed.


An advanced approach has been developed, tested, and implemented on more than 66,000 miles of pipelines. During testing, previously qualified and quantified AC threats, investigated using more comprehensive site and engineering technologies were used as a comparison. This paper addresses how the AC threat assessment can be used in conjunction with readily available pipeline operator data, in-house expertise, as well as other publicly available source data, to compute the level of the AC threat and determine critical regions depending on the operational mode of AC threat, quickly, effectively and accurately in an extremely cost effective manner, and in line with the pending PHMSA Mega Rule part 2 (RIN2).

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AC interference studies have become increasingly popular in an industry where shared right of ways have increased and there has been a better understanding of how AC interacts between pipelines and powerlines that are collocated with each other. While modeling software for AC interference studies have been developed since the 1990s, advancement in AC interference processes have occurred as more has been learned over the years. When performing an AC interference study there are three steps that need to be completed: field data collection, modeling, and mitigation design. Within this paper, we can compare a project from ten years ago to a project from today to understand the developments that have been made over the course of time to improve the way we develop our mitigation designs.
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In most engineering and scientific applications, machine learning (ML) or artificial intelligence (AI) methods in general, are primarily oriented to design a statistical/heuristic procedure to predict the outcome of a system under new conditions. This mechanism aims at exploring non-evident correlations between inputs and outputs that are embedded in the data. However, a large body of this effort relies on black-box function approximations (e.g., neural networks) that have shown limitations to elucidate additional insights from the underlying physical process that generated the data. Thus, this type of knowledge is generated in a data-driven manner without fully explaining the physics governing the problem.
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