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.

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.

Petrophysics sits at the center of every subsurface decision — but it only delivers value when it's properly integrated with geology, reservoir engineering, and drilling. This 4-hour live workshop takes you through the full petrophysics workflow: from how data is acquired downhole, to how logs and core are evaluated, to how petrophysical inputs feed into reservoir models. You'll learn how a petrophysicist actually works inside a subsurface team — what data to trust, how to handle uncertainty, and how to turn raw measurements into decisions that hold up. Whether you're a geoscientist, reservoir engineer, or early-career petrophysicist, you'll leave with a clear picture of how the discipline works end to end.

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.

Most companies use only a fraction of the information locked inside their reflection seismic data. This workshop covers the advanced techniques that unlock the rest.Over five hours, Dr. Andrew Ross walks through how modern seismic methods go beyond structural mapping into true reservoir characterization. The session covers seismic attributes — what they measure, which ones to trust, and how to use them for reservoir delineation; AVO analysis — the physics behind amplitude variation with offset and how it identifies fluids and lithology; and seismic inversion — turning reflection data into rock property volumes for quantitative interpretation. Along the way, he highlights the common pitfalls where these techniques mislead, and shows real examples of how they reduced risk on actual exploration and development decisions.The session is entirely theory and case-study based — no software required. Participants leave with a clear understanding of what each technique can and cannot do, and how to ask the right questions of their seismic data and their service providers.This workshop is ideal for geoscientists, reservoir engineers, and technical managers who work with seismic data and want to understand advanced interpretation methods without getting buried in software workflows.

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.

An exclusive three-day live masterclass designed for practicing petroleum engineers and reservoir specialists who want to go beyond textbook theory. Covering commercial maturity of reservoirs, environmental economics in oil & gas, and polymer flood diagnostics, this masterclass brings four decades of real field experience into your screen — in just 90 focused minutes a day. Available only to PEA Members.