Production Modeling and Optimization with Nodal Analysis
Premium Workshop
For PEA Members Only
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.
Premium Workshop
For PEA Members Only
Deep Learning, Scientific Machine Learning and AI for Reservoir & Production Engineering
Deep Learning, Scientific Machine Learning and AI for Reservoir & Production Engineering is an advanced workshop designed for petroleum professionals who want to explore modern AI-driven methods for solving complex subsurface problems. The workshop focuses on the practical use of deep learning, scientific machine learning, and artificial intelligence in reservoir and production engineering workflows, with emphasis on real-world engineering applications rather than theory alone.Participants will gain exposure to how these methods can be used to improve prediction, accelerate analysis, and support better engineering decisions across areas such as reservoir characterization, production forecasting, and workflow automation. The workshop is structured to help attendees understand where AI adds value in oil and gas operations and how it can complement engineering judgment in subsurface applications.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.