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.

Workshop Objectives

By the end of this workshop, participants will be able to:


Understand the role of the petrophysicist within a petroleum engineering subsurface team


Explain the downhole data acquisition environment and its effect on measurement quality


Evaluate wireline, LWD, and advanced logging data with confidence


Use cased hole data for well & reservoir surveillance and management (WRSM)


Plan core data acquisition and apply core results in formation evaluation


Handle uncertainty systematically in petrophysical interpretation


Prepare petrophysical inputs for model building and upscaling


Apply facies and saturation-height modeling concepts in reservoir characterization

About the Presenter

Joe Bildstein, MEng, is Principal Petrophysicist Consultant at TRACS International with over 30 years of experience in petrophysics and geomechanics. He spent 17 years at Petroleum Development Oman (PDO) as Lead Petrophysicist and Graduate Coach, where he led formation evaluation for clastic and carbonate oil and gas fields, built pore pressure and fracture gradient models, served as SME for cement bond and corrosion logging, and coached PDO's graduate development program from 2016 to 2024. Before PDO, he was Senior Petrophysicist at RWE-DEA (Hamburg & London), working reservoirs across the North Sea, Libya, Egypt, and Germany, and spent nine years with Schlumberger across Venezuela, the USA, Saudi Arabia, Yemen, and Austria. He holds a Master of Petroleum Engineering from the Technical University of Clausthal, Germany, has published with SPE, SPWLA, and EAGE, and was named Best Speaker at the SPWLA regional conference in Muscat. He has trained subsurface staff up to master level throughout his career.

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Material Balance Modeling
November 28, 2026 - November 29, 2026
Material Balance Modeling

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.