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:
-
My story of how I got started in engineering and ended up as a professor at The University of Texas at Austin My Story
-
More about my experiences and my musings My Musings
-
Nothing is possible without awesome graduate students My Students
-
I've written a bit, here's the books My Books
-
My peer-reviewed publications My Papers and My Other Publications
-
I do quite a bit on social media My Social Media Efforts
-
I post a lot of code and course material to support anyone that wants to learn My GitHub
-
I also blog a little, check it out here My Blog and My Other Blog
-
I partnered with Prof. John Foster (UT Austin) and Bazean, a technology-enabled energy investment firm, to start the energy-focussed data sceince education company, daytum. We are currently offering short courses in Energy Data Science.
-
Geostatistical Workflows for Unconventional Reservoirs at BEG
-
What Does a Geoscientist Need to Know About Geostatistics? And Why It Would Be Helpful?
- GeostatsPy: Reimplementation of GSLIB in Python
- Confidence Intervals and Hypothesis Testing with GeostatsPy
- Monte Carlo Simulation with GeostatsPy
- Bootstrap with GeostatsPy
- Data Distributions
- Declustering with GeostatsPy
- Indicator Kriging with GeostatsPy
- Kriging with GeostatsPy
- Multivariate Analysis with GeostatsPy
- Overfitting Models with GeostatsPy
- Plotting Spatial Data with GeostatsPy
- Directional Spatial Continuity with GeostatsPy
- Spatial Updating with GeostatsPy
- Data Transformation with GeostatsPy
- Spatial Trend Modeling with GeostatsPy
- Multivariate Feature Ranking with GeostatsPy
- Variogram Calculation with GeostatsPy
- Variogram Modeling with GeostatsPy
- Spatial Bootstrap
- Probability Theory – my undergraduate lecture
- Statistics – undergraduate lecture
- Marginal, Joint & Conditional Probability – slides
- How to make them in Excel
- Poisson distribution in Excel
- Gaussian transform in Excel and Python
- Log normal distribution in Excel
- Difference in means in Excel and in Python
- Difference in variances in Excel and in Python
- Difference in distributions in Excel
- The Coin Problem from Sivia (1996) in Excel
- Bayesian updating with Gaussian in Excel
- Probability given a positive test in Excel
- Bootstrap in Excel, in Python and in R
- Spatial Bootstrap in Python
- Linear regression in Excel and in R
- Loss functions in Excel
- Multivariate Analysis
- Making an example well in Excel
- Lorenz coefficient in Excel
- Hurst coefficient in R
- Ripley Cross K in R
- Dimensional reduction in Python and in R
- Decision tree in Python and in R
- Support vector machine in Python
- Feature Engineering
- Linear Regression
- Naive Bayes Regression and Classification
- Principal Components Analysis
- Ridge Regression
- Support Vector Machines
- Time Series Analysis
- Clustering
- k Nearest Neighbour
- Neural Networks
- scikit learn Overview
- GeostatsPy: Reimplementation of GSLIB in Python
- Introduction to Data Analytics, Geostatistics and Machine Learning Undergraduate Lectures (Lec00-Lec21)
- What Does a Geoscientist Need to Know About Geostatistics? And Why It Would Be Helpful? and PPT
- Exercises, hands-on and demonstrations PPT Inventory
- Functions that reimplement or call GSLIB exes in Python
- Demo of the functions in Python
- Declustering in Python and with PyGSLIB Package
- Declustering and Debiasing in Excel
- Variogram calculation in Excel and in R
- Full variogram Calculation and Modeling in Excel and in PyGSLIB Package
- Facies criteria in PPT
- Value of quantification in PPT
- Stationarity in PPT
- Uncertainty in PPT
- Suggested books in PPT
- Simple kriging in Excel and in R
- Uncertainty Away from Data in Excel
- Convolution methods in Python
- LU Simulation in Pyton
- Sequential Gaussian simulation in Excel and in R
- Truncated Gaussian simulation in Excel
- Spatial uncertainty in Excel
- Volume-variance relations in Excel
- Working with realizations in R
- 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 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