How Oil and Gas Companies Can Reduce Costs Now With AI


Fluctuating oil prices and the COVID-19 pandemic continue to spell trouble for the oil and gas industry as they look to innovative cost-reduction strategies to stay competitive. Back in March 2020, U.S. crude finished at an 18-year low of $20.09 per barrel. Just one month later, as demand dried up due to coronavirus, US oil prices turned negative. For example, the price of a barrel of West Texas Intermediate, considered the US oil benchmark, dropped as low as minus $37 a barrel. This is the first time in history that the WTI crude dropped into negative territory. In other words, the oil and gas industry experienced a shock like no other in its history.

Now more than ever, oil and gas leaders need to curb costs and build resilience in the face of uncertainty. Artificial intelligence and machine learning-based solutions have proven time and time again to be effective in cutting costs across multiple industries, with the same cost-reduction strategies also proving to boost productivity. With interest at an all-time high, let’s talk about a few ways your oil and gas company leverage AI and machine learning to reduce operating costs.

offshore oil and gas rig

Curb Downtime With AI-Powered Predictive Maintenance 

Back when the price of a barrel of oil was high, there wasn’t necessarily a need to abandon traditional maintenance methods so long as profit margins remained wide. During these trying times, that’s no longer true. One of the biggest sources of lost revenue is unplanned downtime when a critical asset goes kaput. 

A critical asset failure on an offshore production platform can wind up costing an oil and gas company millions of dollars. In fact, just 12 hours of downtime for a 200bpd offshore production platform can result in $6-8 million in lost production opportunity alone. This is catastrophic even when markets are stable; imagine if this happened to your company today as the world’s gone haywire. Without the proper technology, operators are unable to determine the condition of equipment before it’s too late. AI-powered predictive maintenance lets you plan ahead so asset failures don’t catch you (and your wallet) off guard.

While this may seem daunting, the good news is that most offshore platforms today have already invested in sensors across their operations that generate massive volumes of data. By making use of the data that’s already available, offshore operators can leverage a predictive analytics solution that enables predictive maintenance capabilities. 

As we discussed in a previous blog, AI-powered predictive maintenance analyzes data from sensors placed on an asset. Through machine learning-based predictive analytics, the solution can proactively identify anomalous behaviors that deviate from the norm and adapt to asset changes. This increases the ability to identify production-impacting events by as much as 75%. Its predictive models continuously learn over time in order to analyze patterns and flag impending failures far ahead of time, providing asset failure predictions with an average of nine days of advance notice—and in some cases, weeks. Combine with natural language processing technology, operators can access other sources of data beyond sensors, such as maintenance records, to extract the most relevant information and streamline repairs. But how cost-effective is all of this compared to other maintenance methods? Let’s break down the numbers.

Annual costs of maintenance approaches, as reported by the Electric Power Research Institute:

  • Scheduled maintenance: $24/horsepower
  • Reactive maintenance: $17/horsepower—though this is without taking into account the additional costs of safety hazards or operational damage from an asset failure 
  • Predictive maintenance: $9/horsepower, including no hidden costs or dangers

Depending on the size of your operations, that’s a stark difference that can wind up saving your platforms tens of millions of dollars throughout the year, not to mention protecting your offshore employees from serious injury. While predictive maintenance can be done without the use of AI and machine learning, these technologies alleviate many of the difficulties associated with predictive maintenance and will save you considerable time and money in the long run.

Identify Non-Productive Time and Invisible Lost Time with Natural Language Processing

offshore oil rig sunset

Another cost reduction application revolves around eliminating some of the great enemies of oil and gas operations: non-productive time and invisible lost time. Non-productive time results in a whopping 20 to 25% of all rig operating time each year, which can add up to billions of dollars in lost revenue for offshore rigs. Invisible lost time (ILT) can be even more devastating to your bottom line if the proper processes aren’t in place. Invisible lost time refers to small tasks and inefficiencies that are too short on their own to be recorded, but that in aggregate eat up time throughout the day, leading to nonproductivity and thus threatening your profit margins.

But just as AI-powered predictive maintenance can minimize unscheduled downtime, natural language processing automates processes—such as analyzing information contained within rig activity logs so offshore rigs can maximize production. By analyzing rig activity logs, natural language processing can help give operators better insight into what’s happening on their rigs, including where time and resources aren’t being spent well. This technology can categorize rig activity and label each activity with a code and sub-code for ease of analysis that’s much faster and more accurate than humans, ensuring fewer errors when sorting through the data. What was once a subjective, labor-intensive process equivalent to one full-time job can be automated so operators can focus on other tasks to help their companies minimize costs and maximize production. Note that technologies like natural language processing don’t eliminate the need for human operators; technology and humans working together is what brings big productivity gains, especially in times like today.

Oil and Gas Companies Must Act Now to Reduce Costs

The oil and gas industry has been slow to adopt AI technologies, but unprecedented times call for swift actions, and that means turning to innovation and cost reduction applications like predictive maintenance and NLP to come out strong on the other side. AI isn’t a fancy new toy to save for the good times; it’s a critical tool for strategic cost reduction that your operations need to remain competitive and weather this storm.

Contact us at today to learn

how to reduce operating costs and build resilience.

Originally published April 2020.

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