The role of generative AI in the oil and gas sector

The number of new energy assets to be managed is increasing, with overall renewable energy capacity additions globally growing by nearly 13 percent in 2022. The energy system is also more distributed and decentralized due to limited land availability and regulatory obstacles like distance rules, biodiversity concerns, and costs. Even when land is accessible, strategic placement of developments becomes challenging. Renewable energy sources must be linked to aging infrastructure, the demand for CCS is growing (130 commercial-scale CO2 capture projects were announced in 2021), and volatility in the global energy markets adds complexity to an industry that is moving toward electrification.

How might generative AI help address these challenges?

“A digital twin elevated with Generative AI can bring that last mile of contextualisation, giving users a more flexible and intuitive way of interacting with vast amounts of data.” – Haavard Oestensen, EVP & Chief Commercial Officer at Kongsberg Digital

Building a solid data foundation

To grasp the potential of generative AI in the energy sector, consider the foundational role of digital tools and data. There are innumerable data points across the value chain. Imagine the wealth of information involved in a major plant turnaround, including engineering specs, plant conditions, schedules, crew availability, and more. While operators possess vast and valuable data, it’s often distributed across disconnected systems, ontologies, and models. The issue isn’t the volume of data but rather its effective utilisation. Unlocking the potential of this data is challenging. In this context, a powerful solution emerges: the digital twin.

What’s the difference between AI and generative AI?

AI uses content that already exists to analyze and identify patterns, and then prescribe actions. Generative AI can create completely new content with limited information – like a single sentence, or just one word. Generative AI also requires foundation models trained on enormous data sets. But much like a digital twin, once the foundation is in place, the opportunities to build out applications are endless.

Data, and technology that makes data useful, lies at the core of the energy transition. The right technology unlocks the full range of asset performance data, in a way that makes sense to users and stakeholders. It provides an intuitive and actionable way for people to access and use information. It forms the foundation for business owners and operators to pivot from driving incremental production optimizations to incorporating AI that sees increased ROI, improved energy efficiency, and beyond.

A fully digital context

By having data available and mapping business needs to workflows and use cases where the digital twin can bring value that scales, businesses can witness the true strength of a digital context. With a solid data foundation presented in a digital twin where users can plan, execute, and close out entire workflows from start to finish, a fully digital context opens new possibilities for remote surveillance, support, and control.

“This fully digital context is the perfect playground not only for operational decisionmaking and asset management, but also for robotics and drones, sensors, virtual reality, generative AI – the options are endless,” Haavard Oestensen, EVP & Chief Commercial Officer at Kongsberg Digital, says.

Generative AI in action

When a combination of data standards, fuzzy rule matching, and a powerful data graph are present, a digital twin is well-appointed to deal with the myriad of complications like different naming conventions, indirect references, and misspellings that arise when it comes to data ingestion and contextualization. However, there are always outliers, like data that might be missing or misplaced.

Based on experience and knowledge of a particular facility, a human operator may be able to find the connection and fix the outlier, but programming a rule to catch these is more challenging. That’s exactly where generative AI comes in. By using natural language processing on humanreadable text – for example, found in the description of a tag – and matching this to examples found by queries, your digital twin can begin to suggest automatic proposals for fixing this data. Over time, this can help repair the dataset for the operator in a semi-automatic fashion. And when metadata is insufficient or unstructured data is too complex, Generative AI and natural language algorithms can extract this information in mere seconds.

“Unstructured data holds a wealth of valuable information, just waiting to be discovered. With the latest advancements in natural language understanding and Generative AI, we can harness this potential to improve data contextualisation and drive innovation that accelerates the energy transition,” Eivind Roson Eide, Senior Director Kognitwin Product Development at Kongsberg Digital, says.

The growing potential of generative AI

Generative AI and natural language will not only benefit data contextualisation but also make great strides in improving the human-technology experience. Possible use cases include:

Giving users data they might not even know to ask for.

Feeding through alerts from other systems and tools unprompted and based on a trigger that a human might have missed.

Building complex multi-step queries and configuring custom dashboards for particular use cases. Understanding spoken language and converting that to direct control of a user interface. Summarizing large amounts of information from different sources to make important information more readily available to an operator.

The tech-driven energy transition holds the powerful potential to lower repair costs and minimize emissions, increase production, improve drilling efficiency, and limit equipment downtime – all the things that operators worldwide strive to achieve as pressure mounts for near-net-zero operations and working environments that are smarter and safer than ever before.

Use cases for energy and maritime are already being tested. Generative AI has joined the industrial transformation journey and will play an increasingly vital role in ensuring the seamless availability of business data for informed decision-making and efficient work execution. It is well poised to have a substantial material impact on existing and new ways of working, ushering in new dimensions of efficiency, reliability, and sustainability in industrial processes.

The Industrial Work Surface is an end-to-end dynamic digital twin ecosystem where end users are at the center of intelligent assets, perfectly positioned to access the information they need. Get in touch to see what our AIinfused Industrial Work Surface can do for you and your business, today and in the future.