The oil and gas industry generates extraordinary amounts of data from millions of sensors, yet only a tiny fraction, at most 8%, is actually used to inform decisions on complex and valuable assets. Decades of building analytics and machine learning solutions have helped, but they've also left companies with a patchwork of siloed systems and "industrial gridlock."

The arrival of foundation models in late 2022 introduced the possibility of moving beyond one-off solutions. But generic internet-trained models are not suitable for high-risk industrial environments, where accuracy, context, and explainability are essential. The sector needs something different.

Applied Computing is tackling this challenge head-on by creating a foundation model designed specifically for energy. Built to handle time-series data, diagrams, operator logs, and unstructured engineering information, their model emphasizes contextual understanding, explainability, and zero hallucinations.

My guest this week, Dan Jeavons, is President of Applied Computing and former VP of Computational Science and Digital Innovation at Shell. Dan shares his career journey, why foundation models represent a turning point for the industry, and how energy can finally begin to unlock the 92% of data it currently leaves on the table.

👤 About the Guest

Dan Jeavons is President of Applied Computing, a technology company developing foundation models tailored for the energy sector. At Shell, he led global AI initiatives and oversaw advanced research into digital technologies. With over 20 years of experience in consulting and energy, Dan has been at the forefront of applying data and AI to improve business processes, optimize operations, and explore new business models.

LinkedIn: Dan Jeavons

Applied Computing

⚒️ Additional Tools & Resources

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⚠️ Disclaimer

The views expressed in this podcast are my own and do not constitute professional advice.