Introduction to Dynamic Well Modeling and Simulation
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This hands-on workshop skips generic theory and focuses on the transient phenomena that directly affect well uptime. You will learn the fundamentals of transient flow in oil and gas wells and how to build, run, and interpret dynamic models. Dynamic modeling is the natural next step after steady-state simulation — it unlocks the full potential of your well-reservoir asset through parametric studies and risk assessment.
Workshop Objectives
Introduction to dynamic modeling tools
Modeling complex production problems
Applied flow assurance
Dynamic integrated reservoir-wellbore modeling
Hydraulic performance in pipelines
Slow and fast transience in wells
Reservoir pressure decline connected to inflow performance
Well startup and shut-in simulation
Wellbore storage simulation and fluid segregation in oil and gas wells
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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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.