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.

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 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.

Material balance is one of the most direct methods available to a reservoir engineer for estimating hydrocarbons in place, identifying the drive mechanism and forecasting reservoir performance. It requires far less data than a full numerical model, and when it is set up correctly it produces results that can be defended in technical reviews and reserve audits.This workshop covers the complete material balance workflow as it is applied on producing assets. It begins by placing the reservoir within the integrated production system, then moves through data preparation and quality control, tank model construction, drive mechanism identification, and the use of analytical and graphical diagnostic tools. Participants work through the classical diagnostic plots, including Dake and Campbell, and learn how to read what each plot indicates about depletion, gas cap expansion and water influx.The second part of the workshop addresses history matching using both analytical and graphical techniques, determination of STOIIP, aquifer identification and sizing, and running prediction cases to generate production forecasts and recovery estimates. Results are compared against volumetric and simulation-based estimates so that participants understand where material balance is reliable and where a numerical model becomes necessary.The workshop is delivered at an advanced level and is intended for engineers who already work with production and pressure data and want a structured, repeatable method for in-place volumes, drive mechanism evaluation and performance prediction.

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.

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.

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.

This workshop provides a comprehensive view of the LNG value chain, from upstream production and liquefaction to shipping, regasification, and end-user markets. Participants will explore the operational risks at each stage, including safety, environmental, financial, and technical challenges, and learn strategies to effectively assess, mitigate, and manage these risks in a dynamic global LNG industry.