The Cost of Unplanned Downtime for Refineries

In many cases, unplanned downtime is unavoidable. Hurricanes, earthquakes or other unforeseen natural disasters—similar to what happened in Texas this spring—are out of the hands of operators (well, their occurrences—preparedness is a different discussion).

That said, there is much operators can control when it comes to system reliability and everything that coincides with asset integrity management, including potentially avoiding the high costs of unexpected downtime due to system failure. 

In this blog, we’ll talk about the ripple effects of unplanned downtime and some real examples, the value of proactive maintenance, and how technologies available today can enable operators to make the best, informed decisions about maintenance and repair work.

 

Affecting Plant Profitability, Safety and Consumer Wallets

So, what exactly are the costs associated with unplanned outages? Well, when we say “cost,” we aren’t just referring to dollars or revenue, so perhaps “consequences” is a better word.

These occurrences are common in the oil and gas industry, and reasons include human error, mechanical/component failure, too much time in between inspections, low-quality inspections, poor data, and the list goes on. In 2019, refineries in the U.S., Canada and Mexico experienced more than 2,000 unplanned outages that affected production, largely due to mechanical failures that weren’t part of a company’s scheduled maintenance. (Reuters, July 2020)

According to the American Institute of Chemical Engineers (AlChE), the cost of missed production for a U.S. refinery with an average-sized fluid catalytic cracking unit of 80,000 barrels per day will range from $340,000 a day at profit margins of $5 per barrel, to $1.7 million a day at profit margins of $25 per barrel, based on a conservative estimate.

Furthermore, a rapid shutdown increases the danger of mechanical damage requiring costly repairs, and unit shutdowns and restarts are also known to reduce energy efficiency, according to AIChE (not only are startups stressful on equipment, but they consume twice as much energy; energy already accounts for over 40% of U.S. refinery costs).

 

flaring

 

Another potential consequence of unplanned downtime is fugitive emissions and pollution (sulfur dioxide, volatile organic compounds, etc.) A single, unplanned shutdown that lasts hours can lead to the release of a year’s worth of emissions into the atmosphere, according to John Hague, Aspen Technology Inc.

As for some real examples of unexpected downtime and the consequences, unplanned refinery outages don’t just affect the company’s profitability, but they affect consumers, too. In 2015, an explosion and fire at ExxonMobile’s refinery in Torrance, California, prompted a hike in gasoline prices, as the refiner produces close to 20% of southern California’s gasoline supply at 117,000 barrels per day. The U.S. EIA reported that spot prices in Los Angeles for CARBOB (California Reformulated Blendstock for Oxygenate Blending) gasoline increased $0.22 per gallon between February 17 and February 23. 

Refinery inspections, data modeling and preventive maintenance is crucial not only for production sustainability, but also for the safety of the plant and its workers. As we have discussed in a previous blog, in April 2010, at the Tesoro Refining and Marketing Company in Anacortes, Washington, employees were in the final stages of bringing three of six heat exchangers back online, when they ruptured due to long-term damage that went undetected. Several employees working in proximity to the explosion were fatally injured.

While it isn’t 100% certain, it is extremely likely that planned, professional inspections utilizing state-of-the-art technology designed to discover system flaws or wear could have prevented the above scenarios. Adopting technologies that enhance predictive maintenance offers many benefits, especially preventing unscheduled down time and its ultimate cost.

 

Data, Inspections & Predictive Maintenance

Operators using a predictive, data-based approach to maintenance experience 36% less unplanned downtime than those with a reactive approach, according to a 2016 study by Kimberlite. The best way to prevent unplanned outages is minimizing risks, which is done by gaining an understanding of process component conditions and when they are likely to fail (and to perform maintenance, repair or replacement work before that happens). Investing in the resources and technology for risk-based inspections (RBI) that do just that allows operators to make informed decisions about when to schedule an outage or perform repair work, saving them a great deal of lost operating time and money, and potentially prevent catastrophic failure, environmental or personnel safety hazards.

Every new asset, repair, process change, inspection, or excursion must be accounted for and evaluated to determine its impact on the mechanical integrity program and to ensure that an RBI correctly models the damage and risks that may be present. Without updated asset and process information, assets are at risk for unforeseen damage resulting in unplanned downtime. (Inspectioneering Journal, May/June 2021)

Predictive Maintenance to Preventative Maintenance

As explained by Inspectioneering, the fundamental difference between predictive and preventive maintenance is that predictive maintenance is a continuous process based on the current condition of equipment, whereas preventive maintenance is performed in scheduled intervals based on the age and remaining life of a piece of equipment. Both are critical components to asset integrity management and system reliability, but neither can be performant without accurate and timely data.

Robotic technology and its platforms can very quickly, efficiently and safely generate digital versions of assets with large volumes of data in real time, allowing users to obtain advanced analytical and visualization data. With this information, operators can understand how and when to make critical repairs.

 

Gecko Robotics Pressure Vessel Inspection

 

For example, one type of innovative inspection process is Rapid Ultrasonic Gridding (aka RUG), which creates data-rich visual grid maps that identify areas of corrosion and other damage mechanisms. It is 10 times faster than traditional gridding and competing methods. In most situations, the operator can quickly make the decision of whether to proceed with maintenance measures to resolve the issue, or to return the inspected asset to operation.

Gecko's robotic inspections provide some of the fastest whole-asset coverage available on the market. These advanced technologies are equipped to find and collect the critical inspection data you need to make preventative and predictive maintenance decisions, potentially preventing unplanned downtime and all the consequences it brings.

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