pankajcivil/Resources

Resources

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.

The Author:

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.

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

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

Recorded Lectures

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 (Lec00-)

4. Geostatistical Workflows for Unconventional Reservoirs)

5. Geostatistical Workflows for Unconventional Reservoirs at BEG

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

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

GeostatsPy Python Package 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

Resources on Statistics and Probability

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

Parametric Distributions

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

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

Demos of Bayesian Statistics

1. The Coin Problem from Sivia (1996) in Excel
2. Bayesian updating with Gaussian in Excel
3. Probability given a positive test in Excel

Other

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

Heterogeneity

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

Machine Learning

1. Dimensional reduction in Python and in R
2. Decision tree in Python and in R
3. Support vector machine in Python

Geostatistics

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

Supplemental Slides

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.

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