5DayGeostats_DataAnalytics

5-day course on Geostatistics, Data Analytics and Machine Learning

Short course on data analytics, geostatistics and machine learning with lectures, and hands-on demos for a 5-day class.

Course Objectives:

You will gain:

knowledge concerning data analytics, geostatistics and machine learning
theory to improve your understanding
practical workflows to improve your impact

Course Agenda

Introduction: objectives, plan
Modeling Prerequisites - subsurface modeling goals and workflows, probability, statistics etc.
Spatial Estimation – stationarity, variogram calculation, trend modeling, kriging estimation
Spatial Uncertainty - building spatial uncertainty models
Multivariate Analysis – correlation, marginal, conditional and joint, cosimulation, and machine learning
Novel Worklfows - summary of emerging areas of interest
Conclusion – summary, lessons learned, discussion

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

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 [email protected].

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

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