Conclusion#

Michael J. Pyrcz, Professor, The University of Texas at Austin

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Chapter of e-book “Applied Geostatistics in Python: a Hands-on Guide with GeostatsPy”.

Cite this e-Book as:

Pyrcz, M.J., 2024, Applied Geostatistics in Python: a Hands-on Guide with GeostatsPy, https://geostatsguy.github.io/GeostatsPyDemos_Book.

The workflows in this book and more are available here:

Cite the GeostatsPyDemos GitHub Repository as:

Pyrcz, M.J., 2024, GeostatsPyDemos: GeostatsPy Python Package for Spatial Data Analytics and Geostatistics Demonstration Workflows Repository (0.0.1). Zenodo. https://zenodo.org/doi/10.5281/zenodo.12667035

DOI

By Michael J. Pyrcz
© Copyright 2024.

I sincerely hope that this e-book has been helpful. To maximize your learning experience please remember to review the YouTube lectures linked to each workflow to better understand the essential theory and for guidance on best practice and possible applications.

This has been my first attempt to convert my repository of well-documented Python workflows, GeostatsPyDemos: GeostatsPy Python Package for Spatial Data Analytics and Geostatistics Demonstration Workflows Repository [Pyr24b] into an online e-book. Given this source for the content of this e-book, there are natural consequences on the e-book scope, i.e., what is included and what is not? All the workflows demonstrate the functionality of GeostatsPy and the use of GeostatsPy to accomplish fundamental geostatistics workflow steps. Therefore, this is not a general introduction to statistics nor is it a comprehensive treatment of geostatistics. For this I invite you to refer to a standard textbook like, Geostatistical Reservoir Modeling [PD14]. Alternatively, you can review the linked lectures from my Data Analytics and Geostatistics Lecture Series [Pyr24a] for a deeper dive into the theory and practice of geostatistics. Also, I have a recent book chapter that summarizes GeostatsPy, GeostatsPy: Open-Source Geostatistics in Python[Pyr24c] that may be helpful. Recall my stated goal from the introduction.

Welcome!

The goal of this e-book is to teach the application of geostatistics in Python, for those new to geostatistics I provide theory and links to my course content, and for those experienced practitioners I provide example workflows and plots that you can implement.

I am only partially successful in this goal. More can be done! I welcome your feedback and errata, return often and watch this project continue to grow. Yet, I am pleased with this opportunity to expand the reach of my educational content to support stuudents and working professions.

Stay well,

Michael

MICHAEL J. PYRCZ, Ph.D., P.Eng., Professor B.J. Lancaster Professorship and Fellow of the Herring Professorship in Petroleum Engineering Hildebrand Department of Petroleum and Geosystems Engineering and Bureau of Economic Geology, Jackson School of Geoscience The University of Texas at Austin Twitter | GitHub | Website | GoogleScholar | Book | YouTube | LinkedIn

About the Author#

Professor Michael Pyrcz in his office on the 40 acres, campus of The University of Texas at Austin.

Michael Pyrcz is a professor in the Cockrell School of Engineering, and the Jackson School of Geosciences, at The University of Texas at Austin, where he researches and teaches subsurface, spatial data analytics, geostatistics, and machine learning. Michael is also,

  • the principal investigator of the Energy Analytics freshmen research initiative and a core faculty in the Machine Learn Laboratory in the College of Natural Sciences, The University of Texas at Austin

  • an associate editor for Computers and Geosciences, and a board member for Mathematical Geosciences, the International Association for Mathematical Geosciences.

Michael has written over 70 peer-reviewed publications, a Python package for spatial data analytics, co-authored a textbook on spatial data analytics, Geostatistical Reservoir Modeling and author of two recently released e-books, Applied Geostatistics in Python: a Hands-on Guide with GeostatsPy and Applied Machine Learning in Python: a Hands-on Guide with Code.

All of Michael?s university lectures are available on his YouTube Channel with links to 100s of Python interactive dashboards and well-documented workflows in over 40 repositories on his GitHub account, to support any interested students and working professionals with evergreen content. To find out more about Michael?s work and shared educational resources visit his Website.

Want to Work Together?#

I hope this content 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 / or 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-PI is Professor John Foster)? 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. Professor, Cockrell School of Engineering and The Jackson School of Geosciences, The University of Texas at Austin

More Resources Available at: Twitter | GitHub | Website | GoogleScholar | Geostatistics Book | YouTube | Applied Geostats in Python e-book | Applied Machine Learning in Python e-book | LinkedIn