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Energy Analytics Logo

Freshman Research Initiative - Energy Analytics

Course in Energy Analytics

CS 309 - Energy Analytics

Principal Investigator: Michael Pyrcz

Course Objectives

You will gain:

  1. Experience with working with energy datasets
  2. Understanding of the role of data analytics, geostatistics and machine learning for energy applications (subsurface and above surface).
  3. Expert knowledge of methods, workflows and decisions in data analytics, geostatistics and machine learning, and the theoretical and practical considerations, and limitations over the various stages:
    • Data Analysis and Statistics
    • Estimation and Simulation
    • Uncertainty Characterization
    • Decision Making
  4. Expert knowledge of the fundamental algorithms and some ability to customize for advanced workflows.
  5. Understand current practice limitations and new opportunities for advancement of geostatistics.

Prerequisites: None

MEETS: TTH 3:30-5:00 PM Zoom

INSTRUCTOR: TBD

TEACHING ASSISTANT: TBD

Suggested References:

Geostatistics:

  • Pyrcz, M.J., and Deutsch, C.V.,2014, Geostatistical Reservoir Modeling. Oxford University Press.
  • Jensen, J. R., Lake, L. W., Corbett P. M. W., and Goggin, D. J., 2000, Statistics for Petroleum Engineers and Geoscientists, Elsevier.

Statistics / Statistical Modeling:

  • Dekking, F. M., Kraaikamp, C., Lopuhaa, H. P., and Meester L. E., 2007, A Modern Introduction to Probability and Statistics Understanding Why and How. Springer-Verlag.
  • James, G., Witten, D., Hastie, T., Tibshirani, R., 2013, An Introduction to Statistical Learning with Applications in R, Springer.

Additional Instructional Materials

The instructor will distribute field examples in the class or on the course website.

Want to Work Together?

I hope that this is helpful to those that want to learn more about energy analytics, geostatistics, subsurface modeling, geology, and machine learning.