MICHAEL J. PYRCZ, Ph.D., P.Eng., Associate Professor
H.B. Harkings, Jr. Professor of Petroleum Engineering
Hildebrand Department of Petroleum and Geosystems Engineering and Bureau of Economic Geology, Jackson School of Geoscience
The University of Texas at Austin
GitHub Repositories from GeostatsGuy
The best way to learn about sequential Gaussian simulation is to watch it action. The Excel demo demonstrates the steps of Gaussian simulation for simulation at a single location given 3 data.
Need to make an optimum decision in the presence of uncertainty? Try out loss functions to select an estimate that minimizes expected loss in this Excel demonstration.
Need to assess uncertainty over a set of spatially correlated events? python Jupyter Notebook demonstration of spatial bootstrap. based spatial continuity models.
A set of python utilities to support the integration of python Data Analytics, Data Mining and Machine Learning into geostatistical workflows.
A set of Excel demos for statistical and geostatistical methods. May be used for educational purposes. Why Excel? Excel can be used by almost anyone, it is easy to interact with and interrogate the Excel spreadsheet for understanding.
Want to easily calculate a spatially correlated set of random values? python Jupyter Notebook demonstration of convolution based spatial continuity models.
A set of python demos in Jupyter Notebooks for statistical and geostatistical methods. May be used for educational or to integrate in python workflows.