FUTURE DIGITAL TWIN
The role of generative AI in the oil and gas sector The speed of acceleration required to handle a growingly complex energy system hinges on the industry’s ability to continuously adapt, evolve, and integrate new ways of working – with digital tools taking center stage.
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he 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
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.
Building a solid data foundation
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.
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
What’s the difference between AI and generative AI?
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
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