Advanced Well, Reservoir and Facilities Management (WRFM)
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A focused, practice-led program on integrating subsurface, wells, and facilities to lift production, improve recovery, and protect asset value. Participants use real field cases to diagnose constraints, rank opportunities, and implement closed-loop optimization aligned with company standards.
Workshop Objectives
• Build an integrated WRFM workflow linking reservoir, wellbore, and surface network models
• Diagnose production losses using nodal analysis, loss accounting, and structured root-cause methods
• Prioritize surveillance, workovers, artificial lift, and debottlenecking by risk, value, and execution readiness
• Optimize networks and facilities for pressure, capacity, flow assurance, and energy performance with KPIs for sustainment
About the Presenter
Delivered by a senior oil and gas practitioner with cross-disciplinary experience in reservoir engineering, production technology, and facilities optimization. Instruction emphasizes proven WRFM practices, decision-quality analysis, and practical tools that enable teams to progress from diagnosis to sustained performance improvement.
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Machine Learning for Drilling Engineering with Python
Drilling generates huge amounts of data — but most of it sits unused. This hands-on workshop shows you how to turn that data into better decisions using Python and machine learning.Over the sessions, you'll work with real drilling datasets to build models that predict problems before they happen — stuck pipe, ROP optimization, bit wear, formation changes, and more. No heavy math theory. You write code, run it on actual data, and see the results.By the end, you'll have working Python scripts you can adapt to your own field data, plus a clear understanding of where ML adds value in drilling and where it doesn't.Built for drilling engineers, not data scientists. If you can read a drilling report, you can follow this course.