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.

This hands-on bootcamp is designed for petroleum engineers who want to move beyond theory and actually build reservoir engineering and well testing models from scratch — in Microsoft Excel. Over two intensive days, participants work through 10 real-world projects covering the full spectrum of reservoir characterisation and well performance analysis. No black boxes, no third-party software — every model is built cell by cell, so you understand exactly what's happening under the hood. Exclusively available to PEA Members.

Python for Subsurface Engineers: Automating Production & Reservoir Workflows is a two-day live workshop designed for petroleum and subsurface professionals who want to apply Python to real engineering workflows. The sessions will be held on Monday, August 24 and Tuesday, August 25, 2026, at 9:30 PM Indian Time each day, with each session running for 2 hours including a short break.Led by Nashat Jumaah Omar, an oil and gas professional with 12+ years of industry experience, this workshop focuses on practical applications of Python in production and reservoir engineering. Participants will learn how Python can be used to automate repetitive tasks, improve workflow efficiency, handle engineering data more effectively, and support better technical decision-making.All content in this session is copyrighted by Nashat Jumaah Omar. 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, slides, Excel workbook, and certificates will be provided within one business day after the event is completed.

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.

Most production engineering calculations don't need expensive software — they need Excel used well. This live 2-hour beginner workshop shows you how to build the core tools of well performance analysis directly in a spreadsheet. You'll create working models for inflow performance, nodal analysis, and decline curves from scratch, using nothing but Microsoft Excel — so you understand what's happening under the hood instead of treating commercial software as a black box.

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.

This workshop focuses on applying reservoir engineering principles using Microsoft Excel to analyze and manage hydrocarbon reservoirs effectively. Participants will learn to perform key calculations, model reservoir behavior, and optimize recovery through practical Excel-based exercises, enhancing their ability to make data-driven decisions in reservoir management.

This workshop introduces the key concepts and techniques of well intervention operations used to maintain and enhance oil and gas well performance. Participants will learn about various intervention methods, equipment, and safety practices essential for effective well maintenance and production optimization.

This workshop focuses on applying artificial intelligence in full-field reservoir simulation to enhance prediction accuracy, optimize recovery strategies, and streamline reservoir management. Participants will learn how AI-driven modeling techniques complement conventional simulation workflows for faster decision-making and improved reservoir performance evaluation.