Unlocking Value in Oil and Gas: How AI and Digital Twins Are Changing the Game

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In the oil and gas sector, where asset complexity, operational risk, and cost pressure are constants, digital twins and artificial intelligence (AI) are rapidly evolving from innovation experiments to core strategic tools. Together, these technologies are helping companies bridge the gap between operational challenges and high-value outcomes like efficiency, safety, and profitability.

But the road to ROI isn’t paved with tech alone. The real value lies in aligning technology with workforce needs, business goals, and a solid data foundation.

Beyond Buzzwords: What AI-Powered Digital Twins Actually Do

from EY

At their core, digital twins are dynamic virtual replicas of physical assets—from compressors and pipelines to entire platforms. When powered by AI, these twins evolve beyond passive dashboards. They simulate operations in real time, predict maintenance needs, and even recommend actions to improve performance.

One offshore operator uses an AI-powered digital twin to detect early signs of equipment wear. Machine learning models flag issues with gas compressors up to two weeks in advance, preventing costly shutdowns and unplanned maintenance.

But it’s not just about cost avoidance. These systems can simulate operational scenarios on the fly, adjusting separator pressures or flow rates to boost output while minimizing energy use—a clear play for revenue optimization and ESG performance.

Why Some Digital Twins Flop

Despite the promise, many oil and gas companies aren’t realizing the full potential of their digital twin investments. According to EY’s Future of Energy survey, just 14% of companies using digital twins say the technology is meeting expectations.

The culprit? A tech-first mindset. Too many implementations focus on what’s flashy rather than what’s functional. “Starting with technology—without identifying the value proposition—is a recipe for failure,” EY experts found.

To deliver real value, companies must flip the script and focus on what matters to their frontline teams. That means building tools that are usable, useful, and tailored to specific asset-level challenges. The best digital twins aren’t one-size-fits-all platforms; they’re custom-built engines of insight for the people who manage the assets every day.

The Secret Sauce: Contextualized, Reliable Data

Data quality is the lifeblood of any digital twin. But oil and gas facilities produce mountains of complex documentation and sensor data that’s often siloed, outdated, or poorly structured.

AI is helping tackle this long-standing barrier. With generative AI and natural language processing, companies can now contextualize legacy documents, tag engineering drawings, and integrate laser scan data into 3D models.

Case in point: Harbour Energy digitized and tagged more than five million control documents in just three months, unlocking data that had been inaccessible for years.

This contextualization enables not only better decision-making by humans, but also more accurate, reliable AI predictions. It’s what makes natural language interfaces viable in industrial settings—when they can draw from structured, relevant, and validated sources.

Choosing the Right Use Cases

To avoid spinning wheels, digital twin deployments should begin with tightly scoped, high-value use cases. Where are teams losing time? Where do safety risks or inefficiencies lurk? What insights would help drive better outcomes?

Successful use cases include:

  • Predictive Maintenance: Spotting early signs of failure in rotating equipment or pipelines

  • Turnaround Planning: Simulating outages to better plan labor, materials, and timelines

  • Remote Monitoring: Enabling offshore and onshore teams to collaborate via a shared 3D digital twin environment

  • Emissions Reduction: Tracking and modeling leaks or flaring to improve environmental compliance

With a clear problem to solve, the digital twin becomes a trusted tool, not just another screen to ignore.

Talent and Tools: Building for Long-Term Success

Even the smartest digital twin needs smart people behind it. Yet only 46% of energy companies say their workforce has the skills to get full value from current digital twin efforts.

Building a sustainable program requires investment in both infrastructure and people. That includes data scientists, domain experts, and industrial software developers (whether in-house or through partnerships).

Low-code platforms and large language model (LLM)-powered assistants may help democratize digital twin building, enabling engineers to create and modify digital twins without deep programming knowledge. But cultural buy-in and cross-functional collaboration remain critical to scale.

The Bottom Line

Digital twins infused with AI are no longer bleeding-edge tools—they’re increasingly essential to competitiveness in oil and gas. When done right, they offer a rare win-win: enabling safer operations, reducing downtime, optimizing production, and strengthening environmental performance.

But unlocking that value doesn’t start with algorithms. It starts with asking: What problem(s) are we solving? Who needs this? And what data and tools will make it happen?

Oil and gas companies willing to take a pragmatic, people-first approach to digital twin adoption are the ones that will lead—not lag—this next wave of industrial innovation.