Unifying the Oil & Gas Value Chain: How Data + AI Power Performance

Success in the oil and gas industry depends on more than just drilling accurately—it’s about making coordinated, data-driven decisions across the entire value chain. 

From subsurface modeling to refining and logistics, each stage presents its own challenges. But trying to solve each of them with separate tools and teams only creates complexity.

During his recent talk at Data Driven Oil & Gas in Houston, Oxy’s VP/Chief of AI, Patrick Bangert, discussed the complexities of oil and gas operations, emphasizing the need for AI integration at every stage. He highlighted a vision for a more integrated approach while walking us through a relevant and typical use case journey, emphasizing the complexity of coordinating people, equipment, and data, and describing how AI can address challenges in scheduling, maintenance, supply chain, and process optimization. It begins with a subsurface model, refined in real time as drilling progresses. Once the well is operational, decisions on artificial lift systems and pipeline flow require constant adjustment using sensor data and real-time analytics. For example, slugging problems (unsteady flow of oil, water, and gas) can cost operators up to 15% in volume losses—unless they’re addressed with smart, automated systems. Pipeline integrity is another concern. 

This vision is for a different approach: one built on unified data models and centralized decision-making. “The engine that drives this should be common,” Bangert advises.

This is precisely where Kongsberg Digital is helping operators gain an edge. With Kongsberg Digital’s Industrial Work Surface, companies gain a common infrastructure that connects real-time data, AI, and simulation technologies across disciplines and assets.

Instead of stitching together isolated solutions, the Industrial Work Surface enables a unified view of operations—whether it's optimizing drilling based on live subsurface data, choosing the right artificial lift strategy, or automating responses to flow assurance issues like slugging.

Instead of manually reviewing hours of pipeline inspection footage, AI and computer vision can scan for cracks or faults at scale. The same applies to downstream operations, where real-time data and AI help optimize product output (e.g., switching from diesel to jet fuel based on market value).

Kongsberg Digital’s platform supports computer vision and AI workflows that automatically detect faults across thousands of miles of infrastructure. In the downstream space, its data foundation can help operators dynamically shift product output—like prioritizing diesel over jet fuel, as Bangert referenced —based on real-time market signals and operational constraints.

All of this is made possible by a modern, centralized industrial data platform that acts as a digital backbone. It contextualizes and secures data from across the value chain, enabling the scalable deployment of AI models and integrated workflows for production optimization, emissions reduction, and asset performance.

Patrick’s message was clear: solving problems in isolation/silos isn’t sustainable. The future lies in connected systems that power real-time intelligence across the enterprise.

 

Final Thoughts

The biggest affirmation from this session tied to the work we do is that transformation isn’t just about systems or software. It’s about people. It’s about giving teams the tools they need to work smarter, make faster decisions, and stay one step ahead in a complex, constantly shifting industry.

The future Patrick outlined isn’t theoretical—it’s unfolding right now. And we’re honored to be working alongside forward-thinking energy leaders who are imagining what’s possible and actually building it. Together, we’re proving that when the right data, technology, and people come together, real change happens.

 

Read how Kongsberg Digital's Industrial Work Surface helps empower teams here.