N441 - Data-Driven Reservoir Modeling: Top-Down Modeling

Course Overview
This course covers the fundamentals of data-driven analytics and predictive modeling. It explains how to build, history match and validate data-driven reservoir models. Instead of using first principle physics, and geological interpretations, the data-driven reservoir model is developed using field measurements such as well construction details, reservoir characteristics, completion parameters, operational constraints, and production and injection volumes. Data-driven reservoir models have small and highly efficient computational footprints that make them ideal reservoir management tools for the purposes of field development planning and uncertainty quantification.

Participants will learn to:
-  Explain the theoretical background of Artificial Intelligence, Machine Learning and Data Mining,
-  Assess artificial neural networks.
-  Apply genetic optimization.
-  Assess fuzzy set theory.
-  Characterize the different philosophies behind data-driven solutions versus traditional engineering.
-  Conduct data preparation for machine learning purposes using principles of fluid flow through porous media.
-  Apply history matching in the context of data driven reservoir modeling (TDM).
-  Validate data-driven reservoir models (TDM).
-  Apply TDM as a reservoir management tool for field development planning and uncertainty quantification.

This course is offered by Nautilus (www.nautilusworld.com), a PEICE affiliated company and the world leader in Geosciences Training services.

A complete set of course materials and lunches are included.

All scheduled event(s) for this short course:
Date: TBA
Course Syllabus
Download course syllabus
Course Outline
  • Introduction
  • Pitfalls of using Machine Learning in Reservoir Modeling
  • Top-Down Modeling – TDM
  • The Spatio-Temporal Database
  • History Matching the Top-Down Model
  • Post-Modeling Analysis of the Top-Down Mode
  • Examples and Case Studies
  • Limitations of Data-Driven Reservoir Modeling
  • Future of Data-Driven Reservoir Modeling

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Shahab Mohaghegh

Training Venue

To avoid potential course disruptions caused by the attendance of unconfirmed registrants, the training venue address will ONLY be provided to registrants upon receipt of payment (via an email confirmation note).