Module 1

Review of digitalization concept in the energy industry.
Overview closed-loop reservoir surveillance and management.
Outline the needs for employing machine learning and AI solutions that are inspired by physics for subsurface modeling and reservoir monitoring.
Learn the workflows for static and dynamic data inconsistency check with the physics.
Cover Intelligent Data Maid Workflows:
Data reformatting
Data cleaning
Static data processing
Time series signal processing
Anomaly detection algorithms
Data categorization
Data segmentation and partitioning
Spatial-temporal database generation
Case Studies
Module 2

Teach all required concepts related to statistical learning, data mining and cover Intelligent Data Miner Workflows:
Statistical analysis
Generalize linear models development
Advanced dimensionality reduction techniques and algorithms
Clustering algorithms
Advanced KPI identification
Case Studies
Module 3

Learn Intelligent Data Model workflows to build, test and validate the machine learning based models using different shallow and deep learning algorithms as well as hybrid learning concepts.
Learn to interpret the developed intelligent models and gain insight into advanced Intelligent Interpretable Data Models construction.
Uncertainty analysis and optimization for accurate techno-economic analysis.
Case Studies

Module 4

Case Studies

Applied Additional Data Analysis Workshop (for example permeability prediction)

Module 5

Participants Present their Workshop Results and Course Wrap-Up