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Potential predictions
Harnessing generative AI for enhanced scientific research in the energy , oil , and gas industry By Anita Schjøll Abildgaard
In the realm of material science , particularly within the energy , oil , and gas sectors , generative AI ( GenAI ) is making significant strides , not just in hypothetical ‘ moonshot ’ applications as highlighted by McKinsey , but in practical , impactful ways .
When implemented correctly with a focus on cost-efficient domain expertise , GenAI can revolutionize the way scientific literature and corporate patents are summarized and analyzed , providing researchers with timely , actionable , and scalable scientific insights .
If the energy industry is to progress to a more sustainable future , where it can maximize the potential of green technologies and energy storage , leveraging the scientific research available will be key to the transition .
The current landscape
The scientific process entails evaluating most of the literature available , to assess the consensus of the research area in question . This is no different for researchers operating in energy , oil , and gas businesses , who are limited by the number of papers they can read at any given time .
Sifting through vast amounts of data and literature is a time-consuming task that can delay the application of findings to real-world problems . However , artificial intelligence ( AI ) has begun to streamline this process significantly .
By automating the review and summarization of countless scientific documents and patent documentation , GenAI can allow researchers to quickly grasp actionable key findings at scale and integrate them into their projects . This can range from improving safety on oil rigs , as is currently being done by Argonne National Labs , discovering new materials that can improve battery storage capacity or enhance the output of wind and wave turbines .
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