This two-day hands-on workshop is designed for petroleum and subsurface professionals seeking practical skills in machine learning for upstream oil & gas operations. The program teaches participants how to apply machine learning to uncover patterns in production and reservoir data, discover equations from historical relationships, and build predictive models for vital engineering parameters. Real-world datasets from drilling and subsurface domains are used, focusing on automating reports, visualizing data, and interpreting model outputs for improved decision-making.

Workshop Objectives

Introduce machine learning concepts and applications in upstream/subsurface engineering​


Teach Python programming basics for oilfield data processing​


Demonstrate data visualization and pattern discovery using Python libraries​


Enable equation discovery directly from operational and historical data using symbolic regression​


Guide building, training, and testing regression-based predictive models for production parameters​


Explain clustering and dimensionality reduction for grouping wells and simplifying analysis​


Connect machine learning outputs to engineering reasoning and dashboard integration

About the Presenter

Nashat Jumaah Omar - 12+Years of Experience In Oil & Gas Industry

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