Pumpjacks are seen during sunset at the Daqing oil field in Heilongjiang province, China. Long before the current craze surrounding generative AI (Gen AI), the energy sector employed traditional artificial intelligence through the use of data to identify oil and gas deposits. Away from the Microsofts and Googles of the world, a century-old industry—oil and gas—is hoping that Gen AI will make producing petroleum more efficient and easier on its workforce.
Expanding Potential with Generative AI
The energy sector sees even greater potential to save money, reduce accidents, and lower greenhouse gases through Gen AI, exponentially increasing and diversifying the data that can be analyzed. Unlike traditional AI, which programmers and data analysts had limitations in using, the new AI programs can be broadly employed across the workforce.
“Extracting this data from the copious amounts of data generated by drilling activities has historically posed a significant challenge for industry leaders,” wrote Tim Hafke, content marketing specialist with AlphaSense. “That’s where Gen AI comes in.”
In recent years, the downstream industry, which includes refineries that process crude oil into gasoline, has increasingly relied on so-called digital twins—computer-modeled replicas of actual facilities. These digital twins allow companies to run simulations to assess operational issues in real-life facilities, mitigate potential hazards, and perform predictive maintenance (PdM). PdM uses historical and current data to project future performance and determine when to take parts down for maintenance or replace them.
Generative AI’s Role in Enhancing Predictive Models
Microsoft vice president Matthew Kerner sees this as an entry point to generative AI—a way “to explain why the predictive model is making that prediction” and provide context to better address the situation, he explained during a panel discussion at the energy conference CERAWeek. “Gen AI is useful as a bolt-on to some other more predictive AI systems,” Kerner said.
Empowering the Workforce with AI Chatbots
Next-generation chatbots similar to the world-famous ChatGPT can also assist employees on the ground, said Rob McGreevy of industry software company Aveva during the panel. The data-filled chatbot could allow oil field or refinery workers encountering a problem to measure atmospheric conditions like humidity and operational performance like wellhead pressure to quickly diagnose the issue. This enables a quick fix, saving time and money.
Enhancing Safety and Efficiency
During refinery maintenance, “you’re putting people in dangerous situations to do work. If you can do those turnarounds faster, you’re exposing yourself to less risk,” said Matthew Babin, head of energy and natural resources at software company Palantir Technologies. Gen AI can provide “context to people who are in the process of making a decision, even if it’s outside their area of expertise,” Babin said.
Streamlining Maintenance and Repairs
They regularly check or take petroleum installations offline for upkeep. A Gen AI interface provides “access to a maintenance manual, so you can look at how maintenance for that piece of kit should be done,” all laid out in plain English, thanks to a chatbot, McGreevy said. Such a system could also facilitate the repair work itself, taking the guesswork out of such decisions. For example, the technology would allow a company to use a computer model of a facility to determine if there is enough room to use a ladder or install scaffolding, McGreevy said.
Conclusion
McGreevy said it could also help new employees: “I think we can shorten dramatically the time it takes for people coming on board to be proficient to safely operate these facilities at scale.”
Greater efficiencies linked to Gen AI also create a chance to reduce an oil facility’s carbon footprint. However, running the technology also requires huge amounts of electricity, mainly in data centres.
See also: World Intelligence Expo 2024
