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Aprendizaje

Aprendizaje

Aprendizaje

Tutoriales de Software

Python and Pore Pressure Initialization

In this tutorial we will demonstrate how to map a random point cloud with pore pressure values onto the grid points of a FLAC3D model using python.

Tutorial: Simple Slope Stability

Using UDEC 6 and the shear-reduction method to calculate the factor-of-safety, this tutorial will show you how to analyze the stability of a simple slope containing: (1) no discrete jointing (continuum), (2) fully-continuous jointing (discrete blocks), and (3) noncontinuous, en echelon jointing.

Proppant in Fluid Filled Joints

The transport and placement of proppant within fractures is modeled in 3DEC by representing the proppant and fracturing fluid as a mixture.

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Artículos Técnicos

Input to Orepass Design — A Numerical Modeling Study

Orepass design guidelines required for potentially continued mining at depth. Rock strength and stress state were validated through comparison with observed fallouts in orepasses and shafts and the optimal orientation and location of orepasses for future mining were determined.

Flowback Test Analyses at the Utah Frontier Observatory for Research in Geothermal Energy (FORGE) Site

Injection testing conducted in 2017 and 2019 at the Frontier Observatory for Research in Geothermal Energy site in Utah evaluated flowback as an alternative to prolonged shut-in periods to infer closure stress, formation compressibility, and formation permeability. Flowback analyses yielded lower inferred closure stresses than traditional shut-in methods and indicated high formation compressibility, suggesting an extensive fractured system. Numerical simulations showed rebound pressure is not necessarily the lower bound of minimum principal stress. Stiffness changes can be identified as depletion transitions from hydraulic to natural fractures. The advantage if flowback is reduced time to closure.

Blast Movement Simulation Through a Hybrid Approach of Continuum, Discontinuum, and Machine Learning Modeling

This work presents a hybrid modeling approach to efficiently estimate and optimize rock movement during blasting. A small-scale continuum model simulates early-stage, near-field blasting physics and generates synthetic data to train a machine learning (ML) model. Key parameters such as expanded hole diameter, burden velocity, and gas pressure are obtained through the ML model, which then inform a discontinuum model to predict far-field muckpile formation. The approach captures essential blast physics while significantly accelerating blast design optimization.

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