No description, website, or topics provided.
Switch branches/tags
Nothing to show
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Failed to load latest commit information.
2D_MV_200wells.csv Data Files Jul 16, 2018
BayesianUpdatingInversion_Demo.xlsx Excel Hands-on Jul 16, 2018
Bayesian_Gaussian_Demo.xlsx Excel Hands-on Jul 16, 2018
Bootstrap_Demo.xlsx Excel Hands-on Jul 16, 2018
Convolution_Simulation_Demo.ipynb 2 Day Course Exercises Jul 16, 2018
DT_Demo.ipynb August 2018 Updates Aug 17, 2018
DT_demo.R 2 Day Course Exercises Jul 16, 2018
DT_demo.Rmd 2 Day Course Exercises Jul 16, 2018
Declustering.ipynb UPdate Aug 19, 2018
PCA_Demo.ipynb 2 Day Course Exercises Jul 16, 2018
Pyrcz_2Day_Geology_Exercises.docx Update Aug 17, 2018
README.md Update README.md Jul 24, 2018
Resource_Inventory.pdf Resource Inventory Jul 28, 2018
Sequential_Gaussian_Simulation_Demo.xlsx Excel Hands-on Jul 16, 2018
SimpleVariogram_Calc.xlsx Excel Hands-on Jul 16, 2018
Simple_Kriging_Demo.xlsx Excel Hands-on Jul 16, 2018
Spatial_Bootstrap.ipynb 2 Day Course Exercises Jul 16, 2018
Stochastic_1D_por_perm_demo.xlsx Excel Hands-on Jul 16, 2018
Uncertainty_Away_From_Well_Demo.xlsx Excel Hands-on Jul 16, 2018
Variogram_demo.ipynb UPdate Aug 19, 2018
kriging_demo.R 2 Day Course Exercises Jul 16, 2018
kriging_demo.Rmd 2 Day Course Exercises Jul 16, 2018
kriging_demo.html 2 Day Course Exercises Jul 16, 2018
kriging_demo_Rnotebook.ipynb 2 Day Course Exercises Jul 16, 2018
overfit.R 2 Day Course Exercises Jul 16, 2018
post_processing_demo.R 2 Day Course Exercises Jul 16, 2018
simulation_demo.Rmd 2 Day Course Exercises Jul 16, 2018
simulation_demo.html 2 Day Course Exercises Jul 16, 2018
simulation_demo.r 2 Day Course Exercises Jul 16, 2018
unconv_MV.csv Data Files Jul 16, 2018
unconv_MV_v2.csv Data Files Jul 16, 2018
unconv_MV_v3.csv Data Files Jul 16, 2018
variogram_demo.R 2 Day Course Exercises Jul 16, 2018
variogram_demo.Rmd 2 Day Course Exercises Jul 16, 2018
variogram_demo.html 2 Day Course Exercises Jul 16, 2018
variogram_demo_Rnotebook.ipynb 2 Day Course Exercises Jul 16, 2018

README.md

2DayCourse_Exercises

The code, workflows and data for the demonstrations and hands-on components of the 2 Day Short Course, Everything Geoscientists and Data Scientists Need to Know About Geostatistics. The lecture notes are here: https://github.com/GeostatsGuy/2DayCourse.

Bio

Michael Pyrcz is an associate professor at the University of Texas at Austin. He teaches and consults on the practice of geostatistical reservoir modeling and conducts research on new geostatistical methods to improve reservoir modeling and uncertainty for conventional and unconventional reservoirs. He has published over 40 peer reviewed technical articles, a textbook with Oxford University Press, and is an associated editor with Computers & Geosciences. For more details see www.michaelpyrcz.com or follow him on Twitter @GeostatsGuy.

Course Objectives

Class will be accessible to geoscientists and data scientists with no previous experience with geostatistics. We will build up from data integration to spatial estimation and simulation along with uncertainty modeling to support decision making. After completion the students will understand: (1) the benefits and uses of geostatistics, (2) the common spatial and uncertainty modeling workflows, (3) how to better integrate their domain knowledge into the geostatistical model.

Course Outline

For the following 2-day class outline it is assumed that: (1) the class will be taught in English without translation, and (2) 1/3 of time will be guided hands-on practice

  1. Exploratory Data Analysis (a) Sampling theory, stationarity, data debiasing (b) Random variables and functions (c) Univariate statistics, multivariate statistics (d) Geostatistics and big data analytics

  2. Spatial Data Analysis (a) Trend modeling (b) Variogram calculation, interpretation and modeling (c) Scaling relations (d) Training images

  3. Estimation (a) Interpolation (b) Kriging estimation (c) Predrill prediction

  4. Stochastic Simulation (a) Simulation paradigm (b) Gaussian simulation (c) Minimum acceptance and uncertainty checks

  5. Uncertainty Management (a) (Spatial) bootstrap (b) Model post-processing (c) Uncertainty workflows

  6. Machine Learning for Subsurface (a) Estimation variance (b) Multivariate analysis (c) Decision Tree

I am open to revisions to the outline to accommodate student learning needs.