Introduction to Subsurface Machine Learning - ISML

Looking to understand machine learning and how it can be applied to subsurface analytics workflows?

This course is a foundational introduction to the landscape of subsurface-focused machine learning. Topics and techniques covered include outlier detection, data debiasing and imputation, feature engineering, anomaly detection, supervised and unsupervised learning, spatiotemporal modeling, and uncertainty modeling.

Target Audience

Subject Matter Experts with programming experience in Python

You Will Learn

Advanced understanding of geostatistics & machine learning models with subsurface workflows in Scikit-learn & TensorFlow on petroleum data sets.

Course Content

• Probability
• Data Analytics
• Feature Selection
• Feature Engineering
• Machine Learning
• Clustering
• Advanced Clustering
• Dimensionality Reduction
• Multidimensional Scaling
• Naïve Bayes
• k-Nearest Neighbors
• Decision Tree
• Ensemble Tree
• Support Vector Machines
• Neural Networks
• SHAP

Product Details