Integrated Reservoir Characterization
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This workshop provides a comprehensive approach to integrated reservoir characterization, combining geological, geophysical, petrophysical, and engineering data to build robust reservoir models. Participants will learn how multidisciplinary integration enhances reservoir understanding, supports development planning, and reduces uncertainty in decision-making.
Workshop Objectives
• Understand the principles of integrated reservoir characterization.
• Learn methods for integrating geological, geophysical, and petrophysical data.
• Apply multidisciplinary workflows to improve reservoir models.
• Explore strategies to reduce uncertainty and support field development planning.
About the Presenter
The workshop is conducted by a reservoir professional with extensive experience in multidisciplinary integration, reservoir modeling, and field development. The presenter combines technical expertise with practical insight, enabling participants to translate integrated data into actionable reservoir knowledge.
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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.