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.

Workshop Objectives

• Understand the role of AI in modern reservoir simulation and modeling.
  
• Learn how to integrate AI algorithms with conventional simulation tools.
  
• Review real-field case studies demonstrating improved forecasting and optimization.
  
• Explore data-driven approaches for reservoir characterization and performance prediction.

About the Presenter

The workshop is conducted by a professional with experience in reservoir engineering, simulation, and advanced modeling. The presenter has a proven background in applying AI and digital technologies to enhance field development and production performance across various reservoir environments.

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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.