This course provides an introduction to GeostatsPy, an open source Python package for Spatial / Subsurface Data Analytics and Geostatistics, with fundamental spatial data analytics concepts in lectures followed by interactive demonstrations/exercises and more complete example well-documented worklfows.

Before Attending the Course Please Install the Following:

1. Anaconda 3.*
2. GeostatsPy package

For more details see below.

I have included in this repository all of the course content:

1. the lectures as PDFs in the Lectures folder

2. the well-documented and interactive demonstration workflows in Python in the Workflows folder

3. datasets required for the workflows in the Datasets folder

You will gain:

• knowledge concerning basics of the use of the GeostatsPy package for spatial/subsurface data analytics and geostatistics in Python.

• experience with a variety of practical spatial data analytics / geostatistics workflows in Python

The short course is broken up into 5 sections, including:

0. Introduction: objectives, plan
1. Variogram Calculation: quantifying spatial continuity
2. Variogram Modeling: formulating valid spatial continuity models
3. Kriging: spatial estimation
4. Conclusions: summary and feedback

Here's the steps to get setup locally with Anaconda for Python 3.*, common Python packages, Jupyter Notebooks and the GeostatsPy package:

1. Install Anaconda 3.
2. From Anaconda Navigator (within Anaconda3 group), go to the environment tab, click on base (root) green arrow and open a terminal.
3. In the terminal type: pip install geostatspy.
4. Open Jupyter Notebook and in the top block get started by copy and pasting the code block below from this Jupyter Notebook to start using the geostatspy functionality.

import geostatspy.GSLIB as GSLIB
import geostatspy.geostats as geostats

For more information about about the GeostatsPy package check out the documentation and code.

You will need to copy these data files to your working directory. They are available in the DataSets folder of this repository:

• sample_data_MV_biased,csv

• sample_data_biased.csv

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:

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