Subsurface Machine Learning Course

One day subsurface machine learning course.

Course Objectives:

You will gain:

knowledge concerning basic machine learning for subsurface modeling.

Course Agenda

Introduction: objectives, plan
Machine Learning Overview - essential concepts from machine learning
Clustering - theory and practice with hands-on subsurface example
Principal Components Analysis - theory and practice with hands-on subsurface example
Naive Bayes - theory and practice with hands-on subsurface example
k Nearest Neighbour - theory and practice with hands-on subsurface example
Decision Tree - theory and practice with hands-on subsurface example
Ensemble Decision Tree- theory and practice with hands-on subsurface example
Support Vector Machines - theory and practice with hands-on subsurface example
Neural Networks - theory and practice with hands-on subsurface example
Examples and Summary
Conclusions

The Instructor:

Michael Pyrcz, Associate Professor, University of Texas at Austin

Novel Data Analytics, Geostatistics and Machine Learning Subsurface Solutions

With over 17 years of experience in subsurface consulting, research and development, Michael has returned to academia driven by his passion for teaching and enthusiasm for enhancing engineers' and geoscientists' impact in subsurface resource development.

For more about Michael check out these links:

Twitter | GitHub | Website | GoogleScholar | Book | YouTube | LinkedIn

Joined by Didi Ooi from Anadarko Advanced Analytics and Emerging Technology team, assisted with overview content and provided applications in class (not included here).

Want to Work Together?

I hope that this is helpful to those that want to learn more about subsurface modeling, data analytics and machine learning. Students and working professionals are welcome to participate.

Want to invite me to visit your company for training, mentoring, project review, workflow design and consulting, I'd be happy to drop by and work with you!
Interested in partnering, supporting my graduate student research or my Subsurface Data Analytics and Machine Learning consortium (co-PIs including Profs. Foster, Torres-Verdin and van Oort)? My research combines data analytics, stochastic modeling and machine learning theory with practice to develop novel methods and workflows to add value. We are solving challenging subsurface problems!
I can be reached at mpyrcz@austin.utexas.edu.

I'm always happy to discuss,

Michael

Michael Pyrcz, Ph.D., P.Eng. Associate Professor The Hildebrand Department of Petroleum and Geosystems Engineering, Bureau of Economic Geology, The Jackson School of Geosciences, The University of Texas at Austin

More Resources Available at:

GeostatsGuy/Machine_Learning