Production Modeling and Optimization with Nodal Analysis
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Steady-state nodal analysis is still the core tool for understanding how a well's inflow and outflow behave together. But getting real value out of software like PIPESIM or PROSPER takes more than knowing the interface — you need a solid grip on multiphase flow correlations, fluid modeling, and where the bottlenecks in your system actually sit.
In this hands-on workshop, Nashat Jumaah Omar walks you step by step through building, calibrating, and optimizing production systems, and shows you how to construct nodal plots to diagnose and optimize flow from oil and gas wells.
Workshop Objectives
Model setup and fluid characterization (PVT)
Inflow Performance Relationship (IPR) construction and validation
Multiphase flow correlation selection and VLP matching
Single-well nodal analysis and bottleneck identification
Wellhead choke sizing and artificial lift integration (gas lift / ESP)
Sensitivity analysis and production rate optimization
Network modeling and surface facility coupling
Gas hydrate risk prediction and wax deposition mapping
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.