Introduction to Subsurface Machine Learning - ISML


About the Course

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





Product Type:


Formats Available:



If you are interested in a public session of this course, please click the button below to request it.

Request Public Session

This course is also available upon request as a private, on-site seminar. Contact us for details and pricing.

Request In-House Training

Contact us if you have additional questions about how to register for or attend this course.

Contact Us
Print PDF