
Webinar Series
Local AI for Engineers: Secure Drilling Data Analysis with Local LLMs
Webinar Overview
Date: Aug 12, 2026
Time: 11:00am CDT (4:00pm UTC)
Description
Engineers read 5 to 15 reports a day across multiple wells, and most of the real insight is buried in the prose: operations summaries, NPT root causes, remarks the spreadsheet never captures. AI could triage that volume in minutes. The problem is the data that would benefit most from AI is the data that many operating environments restrict from being shared broadly. In this Expert Spotlight Series session, ThinkOnward Community Member Dr. Rami Alloush shows the alternative: running open-weight language models entirely on local hardware, processing data within controlled local environments. He'll cover how to pick the right model for your laptop, set up a free local workbench in minutes, and put it to work live, summarizing a daily drilling report, extracting NPT events into a clean table, and comparing two days of reports side by side. He's just as direct about where local AI falls short, so you walk away knowing exactly where it helps and where it doesn't. A practical, hands-on session for any engineer interested in AI workflows designed for sensitive data environments. The Expert Spotlight Series is ThinkOnward's recurring webinar program built around the expertise inside our community. Each session features a ThinkOnward XIR presenting original work and hard-won knowledge across subsurface science, engineering, and AI. Sessions are free to attend, live, and recorded for registered attendees. August 12, 2026 | 11:00 AM CT | 17:00 UTC
Registration
Speaker

Rami Alloush, PhD
Dr. Rami Alloush is a dual-domain expert in Energy and Artificial Intelligence, currently serving as an Upstream Data Technology Owner at S&P Global Energy. With over 14 years of experience, he specializes in ML-driven data systems and automation, delivering multimillion-dollar enterprise savings through optimized data extraction and delivery. A U.S. patent holder in industrial automation, he bridges complex subsurface engineering with scalable software and agentic workflows. As an AWS Machine Learning Specialist, he focuses on transforming traditional energy operations into high-performance, AI-driven systems.