AI Enhances Reusability of Legacy Geoscience Data

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AI Enhances Reusability of Legacy Geoscience Data

The value of legacy data that may not be immediately available in machine readable formats has been repeatedly demonstrated in oil and gas exploration. Now that value is increasing as operators leverage their inventory of subsurface geotechnical data to move into low-carbon geological energy projects. When data managers stress the importance of well curated metadata to make legacy data more findable, accessible, interoperable, and reusable, some application vendors will pose the question: “Why do we need metadata, can’t we just use ChatGPT?”. After all, the AI data management market is expected to reach US$ 114.99 billion by 2031.

This webinar will explore some of the areas where Generative algorithms, Large language models, Augmented intelligence, and Machine learning (GLAM) can enhance the value of legacy subsurface data. We will also discover why those technologies don’t automatically solve your data problems. Join us to learn how Katalyst is leveraging emerging data technologies to assist companies engaged in geothermal power, carbon and energy storage, native gas exploration, offshore wind and solar, and critical mineral operations.

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Meet the Presenters

Jess Kozman has been a professional data management practitioner since the early 1980’s, specializing in digital data for the resource industry. His roles have included exploration geophysics, IT management, and consulting for national and international petroleum and minerals organizations, government agencies, and service providers. Jess maintains professional qualifications in earth sciences, data quality, and project management. He is currently based at Katalyst’s Americas headquarters in Houston to expand offerings of the consulting service line.

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