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.

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.

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.

Production loss in most wells starts near the wellbore. Drilling fluids, completion fluids, fines migration and scale deposition all restrict flow long before the reservoir itself is depleted. This workshop explains how that damage forms, how to recognise it from well data, and what to do about it — covering the mechanisms behind well stimulation, the main treatment types and where each applies, how to select candidate wells, how formation damage shows up in the skin factor, and why scale deposits and how it is removed in the field. The session is built around field experience and real well cases rather than theory alone.

This workshop provides a comprehensive understanding of Electrical Submersible Pumping (ESP) systems, focusing on design, operation, and troubleshooting techniques. Participants will gain practical insights into optimizing ESP performance and addressing operational challenges to enhance production efficiency.