The Perfect Downtime Storm is Brewing for Oil and Gas Companies this Summer

The Perfect Downtime Storm is Brewing for Oil and Gas Companies this Summer
The Perfect Downtime Storm is Brewing for Oil and Gas Companies this Summer

The COVID-19 pandemic-induced uncertainty continues to trouble the energy industry, compelling enterprises to explore innovative strategies and solutions to maximize their efficiencies and remain profitable. The oil and gas industry was particularly hit due to the declining demand last year. The outbreak of Coronavirus and subsequent lockdowns in most geographies led to demand fluctuation and pushed oil and gas companies to halt or slow down their operations. This caused a severe impact on oil and gas companies' production in both upstream and downstream activities.

Proper maintenance of the refineries is required so they are able to meet the peak requirements. However, reduced demand and profits last year has meant that organizations are reluctant to spend the required amount for the regular overhaul of the equipment and assets.

Maintenance budgets put on the back burner

Reuters' recent report states that top US oil refiners Marathon Petroleum Corp estimate first-quarter spending of $150 million on planned maintenance, reducing by more than half of its previous years' budget. Similarly, Phillips 66 projected $200 million to $230 million in turnaround costs this quarter, versus $329 million a year ago.

By lowering their maintenance budgets and not sticking to the regular overhaul schedules puts them at major risk come the summer time, when demand is expected to return to pre-pandemic levels. A whitepaper by World Economic Forum titled Digital Transformation Initiative by Oil and Gas Industry states that 92% of refinery shutdowns were due to unplanned maintenance, costing the oil and gas companies an average of $42 million a year to $88 million a year in the worst-case scenarios.

Need for an AI-driven cohesive predictive maintenance strategy

As the economies return to normal, the industry needs to swiftly transform itself by leveraging technologies such as Artificial Intelligence (AI) and Machine learning (ML) to build resilience and improve its value-chain. 

Developing a successful predictive maintenance strategy minimizes the disruption and indefinite halt production caused by asset failures. A case in point is a major oil and gas company that leveraged AI and ML benefits to optimize asset utilization, lower energy costs, and improve resiliency.

By leveraging AI, the company enhanced its ability to predict well collapses before they occur, reduce maintenance, efficiently operating the wells, and extending their remaining use of life. The plant’s engineers developed a traffic light system to alert them of the upcoming risk of a well collapse. This allows them to operationalize and put the mechanism in place to reduce downtime. The use of AI helped the plant to bring down the time to revive a well by as much as 83% and the alternative fuel costs by $20,000 per well, per day.

It’s time the oil and gas industry puts AI and ML solutions to work to prioritize predictive maintenance. It will not only allow them to save significantly but also ensure that critical infrastructure is ready and available when needed. The oil and gas companies stand to benefit immensely from AI and ML for more efficient maintenance of their assets.

About The Author

Forogh Askari is an Industry Expert and Technical Sales Leader at Canvass AI, a software provider that makes AI accessible to industrial engineers. In her role, Forogh enables and educates users on the benefits of using AI in industrial operations, thus bridging the divide between industrial process engineers, data science and machine learning. Prior to joining Canvass, she held similar roles at Emerson Impact Partners across Canada for 10+ years, where she worked with manufacturing and oil and gas companies to develop their digital transformation initiatives through process automation and data analytics. Forogh has a Bachelor of Applied Science degree in Chemical Engineering from the University of Waterloo.

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