Geomodeling today is integral to a successful business strategy in most subsurface resource developments including hydrocarbon reservoirs, geothermal and mining extraction. The sub-surface team uses the Geomodel to render the geological interpretation into a digital format suitable for input to resource evaluations, development planning, and to reservoir simulation software as part of uncertainty analysis, and in a variety of decision making processes. A key goal in the Geomodeling practice is to provide images of subsurface heterogeneities critical to better understanding the physical extraction processes for a given resource. Geomodels help reveal the impact of the various reservoir multi-scale features on dynamic behavior in hydrocarbon reservoirs.

The course subjects cover a broad scope of geomodeling practices and trade craft applicable to many resource and reservoir types. The course intent is to provide grounding in fundamental theory, in geomodeling thought process, and to place high level topics into their basic integrated context. By the end of the course, each topic will have been defined and related to general workflows with examples. Best practices, worst practices and pitfalls will be highlighted. Many challenges faced by modelers in sub-surface teams will be discussed. The materials covered are intended to be software vendor agnostic; however, examples and cases are drawn from different software applications solely for the purpose of illustration. Additional reading material will be listed in the notes.

Topics to be defined and discussed:
• Geologic Concepts: facies, heterogeneities, stratigraphic architecture, geometries
• Summary of essential Geostatistical topics
• Geomodeling Cases
• Multi-scale data and rescaling topics
• Properties in Models: discrete and continuous variables
• Modeling Uncertainty: The importance and introduction to approaches
• Post-processing Geomodels: practical use of multiple realizations with objectives
• Introduction to multi-variate data analysis topics, classification and machine learning

Topics to be highlighted -The modeling process has many important steps, best practices and choices:
• Essential statistics and terminology
• Compiling and checking the input data types
• Defining the stratigraphic framework and grid system
• Modeling Methods for Facies, Petrophysics, Permeability, Geomechanics
• Post-processing for summary statistics, uncertainty, and connectivity; well placement
• Volumetric assessments with constraints
• Avoiding bias in results
• Re-scaling methods for the simulator
• Linking static to dynamic behavior