Howdy Folks, I'm Michael Pyrcz, an Associate Professor at The University of Texas. I teach and conduct research on data analytics, geostatistics and machine learning. I'm appointed in the Hildebrand Department of Petroleum and Geosystem Engineering, the Jackson School of Geosciences and the Bureau of Economic Geology. I'm also a principal investigator in the College of Natural Sciences Energy Analytics Freshmen Research Initiative and Inventors' Program and core a core faculty in the Machine Learning Laboratory in Computer Sciences.

I feel that the role of professor is a role of service to our community, so I post all my lectures and supporting content online for anyone in the world to learn. I hope this supports:

• my students with evergreen content long after they finish my courses
• working professionals facing the digital transformation and interested to learn new skills
• potential students by breaking down barriers and making our university a welcoming place for all interested to learn

Here's an inventory of my online resources that I have made to help people learn about spatial data analytics, geostatistics and machine learning. I have produced these resources to support my students and I thought they would be useful to my students after completion of the class (an evergreen resource), to other students and working professionals interested in this topic.

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.

One of my vairous roles is as principal investigator at the Texas Center for Geostatistics. For more about Michael check out these links:

Want to learn more about my story, my publications and other contributions to open source, check this out:

0. My story of how I got started in engineering and ended up as a professor at The University of Texas at Austin My Story

1. My research, approach to research and views on building an inclusive and diverse team My Research

2. Nothing is possible without awesome graduate students My Students

3. I've written a bit, here's the books My Books

4. My peer-reviewed publications My Papers

5. My other contributions My Other Contributions

6. I wrote an open source Python package for spatial data analytics and geostatistics. Much of it is a translation of GSLIB (Deutsch and Journel, 1998) from the original Fortran to Python for 2D geostatistical methods. I did this to support my students in my Spatial Data Analytics and Geostatistics courses. Check it out and consider contributing and become a coauthor at GeostatsPy on PyPi Repository and GitHub.

7. I do quite a bit on social media, here's why I do it, My Social Media Efforts.

8. Check out my TEDx talk on 'A Professor's Secret Weapon' TED Talk

9. Check out my Twitter feed for resources, ideas and possitivity most days, where I'm the GeostatsGuy Twitter.

10. I post a lot of code, demonstration workflows and course material to support anyone that wants to learn My GitHub

11. I partnered with Prof. John Foster (UT Austin) and Bazean, a technology-enabled energy investment firm, to start the energy-focussed data science education company, daytum. We are currently offering short courses in Energy Data Science.

Online Resources on Spatial Data Analytics, Geostatistics and Machine Learning

I record all my university lectures and post them on YouTube. You are welcome to join my classes!

0. Introduction - Howdy, I'm Michael

1. YouTube Channel GeostatsGuy Lectures

2. Introduction to Data Analytics, Geostatistics and Machine Learning Undergraduate Lectures (Lec00-Lec21)

3. Subsurface Modeling Graduate Course (Lec00 - Lec22)

4. Subsurface Machine Learning Graduate Course (Lec00 - Lec18)

5. Open Source Spatial Data Analytics in Python with GeostatsPy

6. Introduction to Spatial Continuity

7. Geostatistical Workflows for Unconventional Reservoirs)

8. Geostatistical Workflows for Unconventional Reservoirs at BEG

9. What Does a Geoscientist Need to Know About Geostatistics? And Why It Would Be Helpful?

10. Center for Petroleum and Geosystems Engineering Webinar - Big Data Analytics for Petroleum Engineering: Hype or Panacea?

11. Michael's Unsolicited Advice and Ideas for a Successful and Happy Career in Our Industry

12. My interview on AAPG's Digging Deeper podcast with the awesome host Vern Stefanic.

I wrote a Python Package called GeostatsPy for spatial data analytics and geostatistics. Here's a set of demonstration workflows in Python Jupyter Notebook for many of the fundamental workflow steps from data preparation, statistical inference to spatial prediction with uncertainty. They go along with my recorded lectures from my courses on my YouTube channels:

• Introduction to Data Analytics and Geostatistics
• Spatial Data Analytics
• Subsurface Machine Learning

Here's the workflows:

0. GeostatsPy: Reimplementation of GSLIB in Python
1. Confidence Intervals and Hypothesis Testing with GeostatsPy
2. Monte Carlo Simulation with GeostatsPy
3. Bootstrap with GeostatsPy
4. Data Distributions
5. Declustering with GeostatsPy
6. Indicator Kriging with GeostatsPy
7. Kriging with GeostatsPy
8. Multivariate Analysis with GeostatsPy
9. Overfitting Models with GeostatsPy
10. Plotting Spatial Data with GeostatsPy
11. Directional Spatial Continuity with GeostatsPy
12. Spatial Updating with GeostatsPy
13. Data Transformation with GeostatsPy
14. Spatial Trend Modeling with GeostatsPy
15. Multivariate Feature Ranking with GeostatsPy
16. Variogram Calculation with GeostatsPy
17. Variogram Modeling with GeostatsPy
18. Spatial Bootstrap

I think interactive workflows are excellent tools to support education. For data analytics and machine learning, turning a dial and watching a system or machine change is a great method to gain intuition and experience. I started to put together interactive workflows with ipywidgets and matplotlib. Check them out here:

1. General Bootstrap
2. Bootstrap Colored Balls in a Cowboy Hat
3. DYI Central Limit Theorem
4. Confidence Interval by Bootstrap and Analytical
5. Sivia's Bayesian Coin
6. Spurious Correlation
7. Correlation Coefficient
8. Neural Networks
9. LASSO Regression
10. Principal Components Analysis
11. Ridge Regression
12. Simple Kriging
13. Stochastic Simulation
14. Uncertainty with Spatial Aggregation
15. Kriging String Effect
16. Uncertainty Model Checking
17. Variogram Calculation
18. Variogram Modeling
19. Spectral Clustering

1. Probability Theory – my undergraduate lecture
2. Statistics – undergraduate lecture
3. Marginal, Joint & Conditional Probability – slides

Parametric Distributions are fundamental to statistics and data analytics inferential and predictive workflows. Sometimes they are required by theory and often they result from nature. Many students struggle with them so I made simple demonstrations in Microsoft Excel that cover how to make them from scratch and how to work with them:

1. How to make them in Excel
2. Poisson distribution in Excel
3. Gaussian transform in Excel and Python
4. Log normal distribution in Excel

Hypothesis Testing is all about recognizing the difference that makes a difference. These tests protect us from the belief in small numbers and are bias to see patterns in random phenomenon.

1. Difference in means in Excel and in Python
2. Difference in variances in Excel and in Python
3. Difference in distributions in Excel

Bayesian Apporaches are powerful. They integrate prior belief with new observations, provide explicit uncertainty models and more intuitive credible intervals for uncertainty in model parameters. Here's some accessible demonstrations to get you started thinking like a Bayesian statician.

1. The Coin Problem from Sivia (1996) in Excel
2. Bayesian updating with Gaussian in Excel
3. Probability given a positive test in Excel
4. Sivia's Bayesian Coin in Interactive Python
5. Bayesian Regression in Python
6. Naive Bayes Regression and Classification in Python

1. Bootstrap in Excel, in Python and in R
2. Spatial Bootstrap in Python
3. Linear regression in Excel and in R
4. Loss functions in Excel
5. Multivariate Analysis

Our subsurface systems are heterogeneous and heterogeneity matters in many subsurface prediction problems. Here are some accessible demonstrations to help you get started quantifying heterogeneity.

1. Making an example well in Excel
2. Lorenz coefficient in Excel
3. Hurst coefficient in R
4. Ripley Cross K in R

I have an new Subsurface Machine Learning Course that builds from fundamental probability to artificial neural networks. The recorded lectures are available here:

• Subsurface Machine Learning

You are welcome to follow along! The demonstration workflows from the lectures are here:

1. Feature Ranking in Python
2. Feature Transformations in Python
3. Feature Uncertainty in Python
4. Dimensional Reduction in Python and in R
5. Clustering in Python
6. Principal Components Analysis in Python
7. Multidimensional Scaling and Random Projection in Python
8. Linear Regression in Python
9. Ridge Regression in Python
10. LASSO Regression in Python
11. Isotonic Regression in Python
12. Bayesian Regression in Python
13. Polynomial Regression in Python
14. Naive Bayes Regression and Classification in Python
15. Time Series Analysis
16. k Nearest Neighbour
17. Decision tree in PythonPython Advanced and in R
18. Gradient Boosting in Python and Advanced Gradient Boosting in Python
19. Support Vector Machines
20. Neural Networks
21. Course Conclusion
22. scikit learn Overview

0. GeostatsPy: Reimplementation of GSLIB in Python
1. Introduction to Data Analytics, Geostatistics and Machine Learning Undergraduate Lectures (Lec00-Lec21)
2. What Does a Geoscientist Need to Know About Geostatistics? And Why It Would Be Helpful? and PPT
3. Exercises, hands-on and demonstrations PPT Inventory
4. Functions that reimplement or call GSLIB exes in Python
5. Demo of the functions in Python
6. Declustering in Python and with PyGSLIB Package
7. Declustering and Debiasing in Excel
8. Variogram calculation in Excel and in R
9. Full variogram Calculation and Modeling in Excel and in PyGSLIB Package

1. Facies criteria in PPT
2. Value of quantification in PPT
3. Stationarity in PPT
4. Uncertainty in PPT
5. Suggested books in PPT
6. Simple kriging in Excel and in R
7. Uncertainty Away from Data in Excel
8. Convolution methods in Python
9. LU Simulation in Pyton
10. Sequential Gaussian simulation in Excel and in R
11. Truncated Gaussian simulation in Excel
12. Spatial uncertainty in Excel
13. Volume-variance relations in Excel
14. Working with realizations in R
15. Lecture on value in industry in PPT

I hope these resources are useful.

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