AI for Reservoir Characterization & Modeling
A two-day technical program on applying machine learning to reservoir characterization, property modeling, and uncertainty quantification.
Course Overview
Machine learning is transforming how reservoirs are characterized and modeled. This program covers ML techniques for property prediction, facies classification, history matching, and uncertainty quantification. Participants work through practical exercises using real-world reservoir data patterns to build intuition for when and how to apply ML in reservoir workflows.
Target Audience
Reservoir engineers, geologists, geophysicists, petrophysicists
Prerequisites
OG-101 or equivalent AI/ML knowledge. Reservoir engineering background.
Learning Objectives
- Apply ML to reservoir property prediction
- Use ML for facies classification and rock typing
- Integrate ML into history matching workflows
- Quantify uncertainty using ML-based approaches
- Evaluate ML results against conventional methods
Key Modules
- ML Fundamentals for Reservoir Engineering
- Property Prediction with Regression Models
- Facies Classification and Rock Typing
- ML-Assisted History Matching
- Uncertainty Quantification with ML
- Integrating ML with Reservoir Simulators
- Case Studies in Reservoir AI
Learning Outcomes
- Practical understanding of ML in reservoir characterization
- Ability to evaluate ML tools for reservoir workflows
- Hands-on experience with property prediction and classification
- A framework for ML-assisted history matching
Your Instructor
Dr. Hussein Mustapha
AITECH Academy Faculty
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