Quantifying the Value of Your Inspections

Introduction

The US refining industry’s average availability realized a 15% increase between 1980 and 2000 as facilities implemented proactive maintenance practices and fixed equipment inspection programs. However, the increases in availability since 2000 have been 2 to 3% despite significant advances in technology that have changed the way the industry collects and analyzes data. Risk-based inspection enabled the industry to leverage relative risk models to focus mechanical integrity (MI) programs, and facilities have access to more software than before to monitor trends and improve data analysis. These relative risk-based models have improved our understanding of where the facility risk is concentrated, but more leading-edge quantitative risk models have several advantages:

1. Ability to quantify the inspection method probability of detecting damage, allowing quantification of the value of an inspection when compared to the inspection-related cost.
2. Ability to link a specific condition monitoring location (CML) to an asset and calculate the impact on asset risk.
3. Ability to quantify the impact on unit or facility performance to calculate the impact of a specific asset’s failure on facility production.

Even in today's data-rich environment, using relative models makes it difficult for MI leaders to confidently define and justify future inspection plans to key decision-makers. Facilities need to begin leveraging advanced modeling to quantify uncertainty and probability of failure (POF), thereby helping to confidently drive valuable inspections and ignore those that provide little or no value.

In this article, we’re going to explore how more advanced modeling addresses the above challenges and drives increased performance for two facilities:

1. Quantifying Business Value of Targeting Specific Condition Monitoring Locations (CMLs) in a Reformer: A refinery leveraged quantitative modeling to quantify the value of its CML placements and optimize its budget on value-adding CMLs. This optimization yielded a high ROI, equating to about $800,000 in total value gain while maintaining or improving asset risk and availability.
2. Quantifying Business Value of Targeted Inspections in a Flare Header: A refinery experiencing leaks in one of its flare headers leveraged quantitative modeling to identify the top contributors to downtime by accounting for the uncertainty associated with different inspection techniques. This optimization resulted in the refiner recognizing a cost savings of $75,000 per flare header system, extrapolated across six units for a total cost savings of $375,000 and a projected 0.4% increase in availability over the next five years.