Unexpected refining outages have skyrocketed in the last few years, with more than 2,000 incidents last year alone—that’s four times the number of outages in 2015. Unfortunately, only a few months into 2020, the trend is continuing, causing insurance costs for U.S. refining and chemical manufacturers to escalate.
Production is at an all-time high, which means a lot of people are postponing planned maintenance downtime in order to capitalize on maximum profits. Essentially, refineries are running full-tilt—often ignoring the potential risks. This trend is documented in data from the U.S. department of energy, which shows five straight years of annual increase in refinery utilization, reaching 93.1% uptime in 2018. In addition to uptime, throughput hit a record high in 2018 of 17.3 million barrels a day.
Analysts from the energy industry expect that once 2019 reports are finalized, they’ll show a continuation of the new record highs for a sixth year in a row—and it’s already raising some concerns among many in industry government, not only for the safe operation of these facilities, but also for the protection of the community and its surrounding environment.
It’s no secret that the number of incidents seen in the industry is alarming. Couple that with reports of operators deferring planned shutdowns for maintenance, and it’s not surprising.
What’s Causing These Problems?
Our industry has a lot of work to do when it comes to preventing unplanned shutdowns, even after two decades of technical innovation and huge steps in predictive sciences. There are several different factors contributing to the increase of these catastrophic incidents, including North America’s aging energy infrastructure. Between that, the retirement of and overall downsizing of skilled maintenance professionals, and the backlog of work many maintenance teams are facing, we have a lot to tackle.
Most maintenance teams are overwhelmed with work orders. The other half of this problem is that there doesn’t seem to be much information available to prioritize the criticality of certain tasks, or the potential risks to the business, plant, people, or the environment if those certain tasks aren’t done.
On one hand, there are so many great, new machine learning technologies at our fingertips. They can transform existing asset management activities, in turn delivering a greater prediction of asset failures—which ultimately helps prevent unplanned downtime. On the other hand, however, we’re still facing the same fundamental issues, despite how far technology has brought us. These fundamental issues are the same ones we were addressing 10 years ago when Asset Performance Management (APM) and predictive analytics solutions were first being introduced.
A comprehensive understanding of risk has always been crucial to an APM software platform’s capabilities. Most APM solution providers’ goal is to calculate the model risk and consequence of failure, from a single component all the way to an entire enterprise. In fact, with a complete view of risk to a business becoming paramount, the role of the CFO has changed a lot in the last 20 years.
In 2011 when Don Schubert, one of the industry’s leading experts on risk, presented at several conferences, the connection between asset management, risk, and the CFO’s office grew substantially. Shubert worked with the largest energy and chemical producers in the world to broker insurance coverage. At these conferences, he educated the audience on just how much money an owner-operator could save if they could accurately demonstrate a thorough understanding of their risk profile.
The overall liability to insurers for global refining and petrochemical incidents from 2017 to 2019? $12.5 million, according to March JLT. A refinery worth $1 billion will likely pay around $2.5 million per year, according to insurance analysts.
A comprehensive approach to asset performance and risk management is an incredibly important component of efficient operation. As Shubert explained, “there is no such thing as zero risk. But if you want to move closer to no risk, you must know risk.”
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