Reservoir Characterization
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This workshop focuses on the principles and techniques of reservoir characterization, integrating geological, geophysical, and petrophysical data to build reliable reservoir models. Participants will gain insights into how characterization supports reservoir evaluation, production forecasting, and field development planning.
Workshop Objectives
• Understand the fundamentals of reservoir characterization.
• Integrate core, log, seismic, and production data for reservoir evaluation.
• Build and interpret reservoir models to assess heterogeneity and performance.
• Apply reservoir characterization in forecasting and development planning.
About the Presenter
The presenter is a petroleum industry professional in reservoir evaluation, geological modeling, and field development. With significant practical experience and a strong background in technical training, the presenter emphasizes the application of multidisciplinary data for effective reservoir management.
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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.