Machine learning for flux regression in discrete fracture networks

In several applications concerning underground flow simulations in fractured media, the fractured rock matrix is modeled by means of the Discrete Fracture Network (DFN) model. The fractures are typically described through stochastic parameters sampled from known distributions. In this framework, it is worth considering the application of suitable complexity reduction techniques, also in view of possible uncertainty quantification analyses or other applications requiring a fast approximation of the flow through the network. Herein, we propose the application of Neural Networks to flux regression problems in a DFN characterized by stochastic trasmissivities as an approach to predict fluxes.

Automated summary

The Discrete Fracture Network (DFN) model is commonly used to simulate underground flow in fractured media, with fractures described using stochastic parameters. Applying complexity reduction techniques like Neural Networks can help predict fluxes efficiently in DFN models with stochastic trasmissivities.