D345 Next Generation Earth Modeling; Integrating Geostatistics, Geoscience, Engineering, and Data Science (Distance Learning)

This course will address questions about using geocellular models for unconventional reservoirs such as:

• The difference between Conventional and Unconventional Reservoirs
• Are common modeling tools such as kriging and conditional simulation appropriate?
• Can Machine Learning be used to create geomodels?
• I’m not a developer, how can I use R and Python to help me build models?
• Is it necessary to model “shale facies” or “geothermal facies” and if so, how do we define them?
• What if my data are sparse and there is no seismic?
• How do I deal with missing values when I am building a geomodel?
• Geocellular grids or meshes; what dimensions, layering types, and granularity are required?
• How to integrate natural fractures - key considerations
• What is the role of micro-seismic data and how can it be used in a geocellular model?
• How can geocellular models be used to improve geosteering and real-time drilling?
• What are the limitations to geocellular grids and stratigraphic layering styles?
• How do geocellular grids of unconventional reservoirs respond in dynamic simulators?
• How can Completion Engineers benefit from geomodels?

The specific topics to be addressed are:

Topic 1

• Introduction
• Role of 3D geocellular models for unconventional resources
• Considerations for constructing geocellular grids or meshes for unconventional resources
• Exploratory data analytics for unconventional resources
• Types of variables
• Univariate and bivariate statistics for data quality assessment
• Review of principles of spatial modeling

Topic 2

• Review of Kriging and Conditional Simulation
• Review of geomodeling steps and the importance of frameworks and stratigraphy
• Workflows for shale plays and how they differ from conventional plays
• Understanding multivariate analysis techniques and machine learning
• Applications in R and Python
• Post processing - Building probability maps to identify “Sweet Spots” and improve well placement

Topic 3 - Modeling Workshop

This workshop uses simple exercises on a variety of multivariate techniques in order for participants to better understand the underlying principles. Some of the examples are from unconventional shale reservoirs and some are not. Those that are not, are classic data sets used in texts, classrooms, seminars, and online short-courses and are not necessarily geologic in nature, but clearly illustrate the methods. 

Practical computer-based exercises demonstrating Multivariate Data Analytics that form the basis of the workshop include the following:

• A brief introduction to R and R Studio
• Exploratory Data Analytics
• Spatial Modeling, Kriging and Conditional Simulation
• Analytics for data reduction and data redundancy
• Principal Component Analysis and Factor Analysis 
• Classification Systems and Regression Systems
• Discriminant Analysis
• CART

The actual number of exercises and methods will vary depending on available time.