chevron uses AI to bring reliable energy to data centers

Artificial intelligence (AI) has wide-ranging benefits. It’s transforming industries and everyday life by increasing productivity, improving decision-making and automating large-scale operations. But AI data centers require a lot of power. Luckily, AI itself can assist in delivering energy that can be used to power these data centers—and much more.

Your experience with AI may involve using a digital assistant like Siri or Alexa to manage your schedule. Or you may have used something like ChatGPT to help you write your resume.

Now imagine that type of assistance used to help an energy company like Chevron analyze data, remotely control equipment or model hydrocarbon reservoirs. Then consider all the companies around the world using AI in other ways to innovate and to streamline their work.

The demand for reliable energy to power the AI data centers that make all of this possible is high. In fact, the International Energy Agency says that data centers and data transmission account for 1% to 1.5% of all global electricity use.

And the agency expects that to double by 2026. This would mean that AI data centers require about the same amount of power as the entire country of Sweden.

Jeff Gustavson, president of Chevron New Energies, discusses how Chevron can work with others to power future AI data centers.

Power reliability is necessary to keep the benefits of AI accessible. That’s why Chevron has teamed up with GE Vernova and Engine No. 1 to develop onsite power plants that can run on natural gas at certain data centers.

While Chevron works to power AI, Chevron employees are also using artificial intelligence to enhance their ways of working. For decades, Chevron has utilized data science and analytics to advance energy solutions, laying the foundation for AI to evolve in this space.

Here are some current applications:

AI underground: Oil and gas companies do a lot of work underground, where visibility and insights can be limited. AI helps paint a more accurate picture of the subsurface than traditional methods. This is important because it helps companies determine which areas are good for oil and gas recovery, which areas might be good for CO₂ storage and more.

Reliability: Keeping assets up and running is imperative in delivering reliable energy. AI helps with that through preventative maintenance. Real-time sensor data and anomaly-detection methods help reveal problem areas such as leaks. Finding these discrepancies early helps crews know where to focus their attention.

For example, Chevron teamed up with Percepto for a six-month pilot using AI-equipped drones to remotely inspect facilities and monitor for anomalies in normal operations.

 

Transportation: When transporting oil and gas, AI can help optimize routes and cargo loads, allowing Chevron to maximize efficiency and safety. This is true for transport via land, sea and air.