Title: Machine learning for graph-based representations of three-dimensional discrete fracture networks,
Abstract: Structural and topological information play a key role in modeling flow and
transport through fractured rock in the subsurface. Discrete fracture network
(DFN) computational suites such as dfnWorks are designed to simulate flow and
transport in such porous media. Flow and transport calculations reveal that a
small backbone of fractures exists, where most flow and transport occurs.
Restricting the flowing fracture network to this backbone provides a
significant reduction in the network's effective size. However, the particle
tracking simulations needed to determine the reduction are computationally
intensive. Such methods may be impractical for large systems or for robust
uncertainty quantification of fracture networks, where thousands of forward
simulations are needed to bound system behavior.
In this paper, we develop an alternative network reduction approach to
characterizing transport in DFNs, by combining graph theoretical and machine
learning methods. We consider a graph representation where nodes signify
fractures and edges denote their intersections. Using random forest and support
vector machines, we rapidly identify a subnetwork that captures the flow
patterns of the full DFN, based primarily on node centrality features in the
graph. Our supervised learning techniques train on particle-tracking backbone
paths found by dfnWorks, but run in negligible time compared to those
simulations. We find that our predictions can reduce the network to
approximately 20% of its original size, while still generating breakthrough
curves consistent with those of the original network.