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Practical Application Of AI To Transform Pipeline Integrity Management

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Management and operating teams, across all industries, but especially inside asset management organizations, must increasingly rely on data-driven assessment and analytics for decision making. Clearly, inefficiency and ineffectiveness associated with manual or poorly optimized business processes and fragmented business management Infrastructures is a barrier to formulating the safest, most efficient and cost effective asset management and operational decisions. According to Deloitte’s digital maturity analysis, the midstream industry and specially the pipeline sector is in its lowest stages of digital adoption (first 20-30% progress).

Product Number: 51323-19039-SG
Author: Pankaj Tandon
Publication Date: 2023
$0.00
$20.00
$20.00

Midstream pipeline integrity teams face continual challenges from deteriorating conditions such as corrosion that can impact their transmission infrastructure. In order to maintain compliance with federal and state regulations, and preserve continuity of operations, operators must integrate across several data sources in their maintenance plans.


This paper will illustrate how Praemia™ Inc. software equips operators with planning tools to better address the dynamic nature of integrity management activities. Powerful analytics incorporate assessment and risk analysis for efficient prioritization of mitigating and remediating measures while balancing system risk and system budget, resulting in safe, cost effective and efficient integrity decisions.


Artificial Intelligence (AI) and Machine Learning (ML) technologies have been applied for years in many industrial fields, but for the pipeline industry, the concept is still relatively new and mysterious. This paper will demystify AI for the midstream pipeline integrity sector with one practical application to dramatically enhance condition assessment, prevention and remediation activities scheduling and transform the way operators can address regulatory and asset decay challenges.

Midstream pipeline integrity teams face continual challenges from deteriorating conditions such as corrosion that can impact their transmission infrastructure. In order to maintain compliance with federal and state regulations, and preserve continuity of operations, operators must integrate across several data sources in their maintenance plans.


This paper will illustrate how Praemia™ Inc. software equips operators with planning tools to better address the dynamic nature of integrity management activities. Powerful analytics incorporate assessment and risk analysis for efficient prioritization of mitigating and remediating measures while balancing system risk and system budget, resulting in safe, cost effective and efficient integrity decisions.


Artificial Intelligence (AI) and Machine Learning (ML) technologies have been applied for years in many industrial fields, but for the pipeline industry, the concept is still relatively new and mysterious. This paper will demystify AI for the midstream pipeline integrity sector with one practical application to dramatically enhance condition assessment, prevention and remediation activities scheduling and transform the way operators can address regulatory and asset decay challenges.

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To ensure the safe operation of a gas processing plant, monitoring the Critical Process Variables (CPV) against the Integrity Operating Window (IOW) is a key element for compliance with the corrosion management framework. This paper is the operator’s experience in setting up the IOW management system in a gas processing plant and benefits gained.
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Integrity Operating Window Development for Refinery Units and Implementation Through a Digital Platform

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Integrity operating windows can be developed to account for any changes occurring during plant
operation that affect expected damage rate or susceptibility to damage.
A five-step methodology based on API RP 584 advice was developed for consistent identification of
integrity operating windows and subsequently applied to several plant units in different refineries.
A digital dashboard was also developed for collecting and trending all the parameters affecting each
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predictive models). This represents a further step towards reliable implementation of integrity
operating windows and their positioning within an asset integrity management framework.
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