Learning

Customized Training

Customized Engineering Training Courses

Itasca Educational Partnership

ITASCA Academics

Software Tutorials

FLAC3D 6.0 PFC Plugin Punch

An Introduction to Python Scripting: Part 1

Introduction to Python scripting by reviewing key concepts and through demonstrations. Part 1 focuses on installing Python, variables and types, conditions and loops, and functions.

Command Conversion Tool

Learn how to automatically convert old FLAC3D and 3DEC data files and FISH functions into the most current software version.

Technical Papers

Connectivity, permeability, and channeling in randomly distributed and kinematically defined discrete fracture network models

A major use of DFN models for industrial applications is to evaluate permeability and flow structure in hardrock aquifers from geological observations of fracture networks. The relationship between the statistical fracture density distributions and permeability has been extensively studied, but there has been little interest in the spatial structure of DFN models, which is generally assumed to be spatially random (i.e., Poisson). In this paper, we compare the predictions of Poisson DFNs to new DFN models where fractures result from a growth process defined by simplified kinematic rules for nucleation, growth, and fracture arrest.

Using MINEDWto simulate pore pressure as input for FLAC3Dand 3DEC

It has become common practice to create a three-dimensional (3-D) geomechanical model for the analysis of rock stability.

A Discrete Fracture Network Model With Stress-Driven Nucleation: Impact on Clustering, Connectivity, and Topology

The realism of Discrete Fracture Network (DFN) models relies on the spatial organization of fractures, which is not issued by purely stochastic DFN models. In this study, we introduce correlations between fractures by enhancing the genetic model (UFM) of Davy et al. [1] based on simplified concepts of nucleation, growth and arrest with hierarchical rules.