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Optimizing Preventative and Mitigative Measure Selection

[+] Author Affiliations
Jeffrey Lachey, Tony Alfano

DNV GL, Columbus, OH

Keith Vanderlee, Robert Jewell

AGL Resources Inc., Naperville, IL

Paper No. IPC2016-64638, pp. V002T07A024; 6 pages
doi:10.1115/IPC2016-64638
From:
  • 2016 11th International Pipeline Conference
  • Volume 2: Pipeline Safety Management Systems; Project Management, Design, Construction and Environmental Issues; Strain Based Design; Risk and Reliability; Northern Offshore and Production Pipelines
  • Calgary, Alberta, Canada, September 26–30, 2016
  • Conference Sponsors: Pipeline Division
  • ISBN: 978-0-7918-5026-6
  • Copyright © 2016 by ASME

abstract

As risk assessment methodologies, tools, and processes continue to evolve in the industry, utilizing risk outputs to not only identify high risk locations, but to also understand the driver(s) behind the elevated risks for those locations is paramount. The ideal scenario for reducing pipeline risk is utilizing a risk-driven mitigation plan as this ensures the optimal use of company dollars, but also inherently means that a company has a firm understanding of their data and pipeline system. When the company understands their data and the implications for its inaccuracies, whether it be improper data alignment or incorrect application of data, they can effectively employ a campaign for preventative and mitigative measures (P&MM). However, if suspect data is used during a risk assessment, P&MM cannot accurately target risk drivers and high risk locations, making it challenging for the company to maximize their resources.

For well over a year, an on-going partnership between AGL Resources Inc. (AGL) and Det Norske Veritas (U.S.A.), Inc. (DNV GL) has ensued to tailor a GIS-based risk management software solution for AGL. Through this collaboration among Integrity Management, Risk Management, IT, GIS, and Operations & Maintenance subject matter experts (SMEs) on both sides, one central hub of cross-functional pipeline knowledge was created. As a result, countless opportunities were exploited to identify supplementary data sources to employ new data manipulation techniques and processes, providing AGL with the foundation for such a risk-based Preventative & Mitigative Measure program.

With the foundation laid and the proper risk elements present, AGL can now execute optimized risk-informed responses to identified high risk locations, pipeline segments, or pipeline systems. These optimized responses require an understanding of the types of P&MM available to reduce the threats and consequences, the costs involved for each P&MM implemented, and the utilization of a tool to allow various ‘what-if’ risk analyses to be conducted. Adopting and integrating this process as part of AGL’s risk management program allows them to capitalize on the maintenance dollars they spend while also reducing the potential hazards to the surrounding people, places and environment.

Copyright © 2016 by ASME

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