Transition to more accurate Risk Analysis

Proceedings Publication Date:

29 Mar 2022
Presenter
Fidel Espíndola Cruz
Company
Author
Fidel Espíndola Cruz, José Samuel Solis Martínez, Carlos Ivan Mancilla Ortíz
Part of the proceedings of
Abstract

Although Risk Analysis is one of the best tools for the decision-making in pipelines management. At the beginning a simple method with indexed results and/or a high/medium/low ranking is a good option, but as any quality process, transition to a more accurate method is the next obligated step. This change represents a huge effort because of the increment in information required, but the results can refine the definition of preventive actions required.

The main idea of this paper, is to share experiences about the evolution of an historical method used for risk analysis, based on the identification of problems to support maintenance projects related to prevent threats with a low statistical occurrence, and at the same time, dealing with an uniform ranking criteria for pipelines with completely different conditions which certainly complicate the decision-making problem.

The problem to evaluate threats independently of its statistical occurrence is solved through a “defining tolerances” process, which includes the validation of previous analysis results, identification in the risk algorithms of all of the variables affected only by maintenance actions, and acceptance limits for each one, as well as new considerations configured in the algorithms used for the risk calculation. All of this supported on regulations and best practices referred to prevention of every single threat in pipelines.

By the other side, the problem of decision making implies to configure some specific algorithms to identify individual limits for every single condition along the pipelines and stablish new Key Performance Indicators (KPI’s) to identify in a better way the specific sections that need some preventive actions.

In conclusion, companies dedicated to maintenance can change the way they analyze and rank risk, independently of historical data, refining tolerances to get more accurate results for different approaches and better supported decision-making process.

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