Course Length:   5 Days        Course Dates:   June 1 – 5, 2020         Course Venue:   Calgary, Alberta, Canada

Course Description:

This course covers the fundamental principles of deterministic and stochastic inverse modeling and their application to calibration of hydrocarbon reservoirs and uncertainty quantification. It covers a variety of topics related to the integration of production and performance data into reservoir models and account for their respective errors and uncertainties. The topics include history matching problem formulation, deterministic non-linear least-squares methods, probabilistic Bayesian methods, iterative and recursive history matching techniques, gradient-based techniques and adjoint method, as well as common techniques for regularization and parameterization of reservoir models for history matching. Applications and case studies from both deterministic and probabilistic history matching will be presented and discussed.

Who Should Attend?

This course is designed for professional reservoir engineers, petrophysicists, geophysicists, geologists and asset managers

What You Will Learn:

• Understand foundations of modern history matching techniques
• Understand foundations of uncertainty management in history matching techniques
• Communicate and implement a consistent uncertainty management policy
• Understand the value of uncertainty management analysis in reservoir development and management
• Develop clear guidelines for making decisions in analysis in reservoir development and management

Course Outline:

• History Matching Problem Formulation
• Linear Inverse Problems
• Regularized Least Squares Inverse Problems
• Nonlinear History Matching Inverse Problems
• Preliminary Material on Stochastic Approaches
• Bayesian History Matching and Stochastic History Matching with the Ensemble Kalman Filter
• Reservoir Parameterization for History Matching
• Case Studies
• Case Study 1: Gradient-Based History Matching
• Case Study 2: Ensemble Kalman Filter for History Matching