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AI and energy are two of the most critical forces shaping the future of our planet—and their relationship is impossible to ignore today.
From the significant power consumption of data centers to the growing energy requirements of AI-driven applications, the rapid adoption of AI is driving a surge in global energy demand that is outpacing the growth of renewable energy sources.
This presents a crucial challenge: How to balance the environmental impact of this technology with the transformative potential it holds? The solution is more AI.
Transform the environmental impact of energy production
As I explored in my recent article, technologies like cloud, edge computing, and AI are reducing the carbon intensity of oil and gas production today while helping to advance the new energy systems of tomorrow.
For example, consider the parallels between data intelligence for energy operations and autonomous vehicles. Much like how self-driving cars interpret real-time data about other drivers and traffic conditions to make decisions, AI-enabled devices in the oil field interpret data from wells and facilities in the network to take proactive and autonomous actions. This ensures that operations stay in the “sweet spot,” unlocking significant productivity gains while reducing costs and carbon emissions.
This is only the beginning. Soon, AI will enable optimization throughout the entire production life cycle—from subsurface exploration to field development and production operations. This will allow us to optimize assets in real time, marking a significant step forward in energy production while maximizing performance and sustainability.
But to realize this vision, we must unleash the full potential of AI across our industry. It must evolve from a digital tool that supports individual tasks into a fundamental capability set woven into the very fabric of our planning, decision making, and operations.
AI will be the X factor for our industry. It has the potential to fundamentally transform the environmental impact of energy production. But for this to happen, we can’t rely on traditional AI and machine learning workflows. We need tailor-made solutions to meet the unique demands of the energy industry. Enter engineered AI.
Engineered AI: AI for the energy industry
The AI lexicon is constantly expanding and now includes everything from narrow AI to general AI to superintelligent AI, alongside the now ubiquitous generative AI. However, the unique challenges of the energy industry demand a specialized approach.
To address them effectively, we at SLB propose “engineered AI”—a specialized approach to AI development focused on solving the energy sector’s most pressing challenges.
Now, you may ask, “Do we really need more AI?” Well, consider this: Before a single barrel of oil or cubic foot of gas is produced, vast amounts of data are generated, analyzed, and acted upon. In fact, a single well can produce more than 10 terabytes of data per day, roughly equivalent to half of the text content in the U.S. Library of Congress.
Engineered AI is purpose-built to address these complexities. It combines machine learning and generative AI with energy-specific data, physics-based modeling, and the deep domain expertise of the scientists and engineers across our industry. With open, secure, and adaptable architectures, we can unlock decades of historical data to drive innovation across the industry.
As engineered AI evolves, it will enable the industry to rapidly accelerate and derisk processes such as reservoir design and management, construction of wells and facilities, and asset maintenance and performance. Ultimately, this will result in greater efficiency, reduced costs, and lower carbon emissions across the entire energy value chain.
AI for the energy transition
While engineered AI will be critical for improving performance and reducing emissions in the oil and gas industry today, it will also play a key role in scaling the low carbon solutions of tomorrow.
Leveraging decades of subsurface data, we are already developing engineered AI solutions to identify optimal locations for carbon capture and storage and geothermal energy developments. This represents a significant step forward in reducing industrial emissions and accelerating the transition to clean, renewable energy systems. And as engineered AI capabilities mature, its impact will continue to accelerate.
So, while the rapid growth of AI undoubtedly introduces new complexities to the global energy mix, I believe AI will unlock new opportunities, becoming one of our most valuable tools in delivering secure, affordable, and sustainable energy for all.
When we get it right, AI isn’t just technology. It’s the key to a world with more energy and less emissions.
Rakesh Jaggi is president of Digital & Integration at SLB.
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