Abstract

In low-permeability fractured media, such as granites and shales, flow and the associated transport of dissolved solutes is controlled primarily by fractures embedded within the rock matrix. Interplay of individual fracture geometry with network structure determines the properties of the fluid flow field therein. However, relevant lengths scales within a fracture network range several orders of magnitude, which makes it is challenging to determine which features of the network influence which flow and transport properties. One tool to investigate the interplay and influence of these multiple scales are discrete fracture network (DFN) models. In this talk, I’ll discuss recent studies that use high-fidelity DFN models, graph theoretical representations, and machine learning approaches that attempt to link flow and reactive transport observations to physical structures of a fracture networks.

Bio

Dr. Jeffrey Hyman is a staff scientist in the Earth and Environmental Sciences Division at Los Alamos National Laboratory. He received his PhD in Applied Mathematics from the University of Arizona in 2014 with a PhD Minor in Hydrology and Water Resources. His research focuses on integrating applied mathematics with the geosciences to advance our understanding of coupled subsurface processes in fractured media. He is an Affiliate Faculty in the department of Geology and Geological Engineering at Colorado School of Mines and the director of the Advanced Computational Geosciences Initiative (ACGI) at Los Alamos National Laboratory. He has published over 100 peer-reviewed articles. Dr. Hyman is the principal developer of dfnWorks (2017 R&D 100 Winner) a leading modeling suite for three-dimensional discrete fracture network simulations.