Data Science and Applied Machine Learning for Reservoir & Production Engineering is a practical workshop designed to help petroleum professionals use data-driven methods to solve real engineering problems. The workshop focuses on how machine learning and data science can be applied to reservoir characterization, production forecasting, well performance analysis, and workflow automation in upstream operations.


The workshop is built around industry-relevant use cases, with an emphasis on turning engineering data into actionable insight. Participants will learn how data science techniques can support faster analysis, improve prediction quality, and enhance decision-making in reservoir and production engineering workflows.


All content in this session is copyrighted by Mr. Nashat. Access to recordings, slides, and the Excel workbook is provided exclusively through this platform. Any unauthorized use, reproduction, or distribution outside the portal is strictly prohibited and may result in legal action under applicable laws.


Recordings, data sets, and certificates will be provided within one business day after the event is completed.

Workshop Objectives

By the end of this workshop, participants will be able to:


Understand the role of data science and machine learning in reservoir and production engineering.


Identify practical use cases for machine learning in upstream oil and gas workflows.


Apply data preparation and feature engineering techniques to engineering datasets.


Use machine learning concepts for reservoir characterization and production forecasting.


Recognize how applied analytics can improve interpretation, monitoring, and decision support.


Build a stronger foundation for using Python-based data science tools in petroleum engineering applications.

About the Presenter

Nashat Jumaah Omar is a production engineer, instructor, and technical consultant with over 11 years of experience in the oil and gas industry, with particular depth in production engineering, flow assurance, subsurface workflows, and engineering data applications. His work combines petroleum engineering knowledge with practical use of digital tools such as Python, SQL, Power BI, C#, FORTRAN, and VBA to improve analysis, automate repetitive tasks, and support more efficient engineering workflows.


Over the course of his career, he has worked across production operations, well and network modeling, flow assurance, production data management, and software-enabled engineering solutions, including experience with platforms such as PIPESIM, PROSPER, OLGA, and OFM. His professional background also includes training and mentoring engineers in Python, machine learning, data analytics, and applied digital workflows for upstream oil and gas environments.


At Petroleum Engineers Association, he has contributed to the design and delivery of technical training programs for petroleum professionals, including courses in Python for petroleum engineering, machine learning, data analysis, and production and reservoir applications. His teaching style is grounded in real industry use cases, with an emphasis on helping participants translate coding and analytics concepts into practical tools for day-to-day engineering work.

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