Unlock the power of drilling data using Gen AI

Identifying drilling risks for upcoming wells requires insights from offset wells. While some risks may lead to non-productive time (NPT), others remain latent within daily drilling reports (DDRs), which is crucial for comprehensive analysis. Manual extraction from DDRs is often time-consuming. This webinar will show the work done by systematically exploring how large language models (LLMs) enhance workflows to automate insights extraction and improve risk identification accuracy.

The presenter is Ahmad Naim Hussein, Global Innovation Manager - Drilling. Ahmad is an SLB drilling emissions business manager based in London and holds a BSc Degree in Mechanical Engineering. Ahmad spent the last 10 years in the Middle East in a variety of digital drilling roles. Prior to that, he worked as a drilling engineer for BP in Oman and Jordan.

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