Deriving three-dimensional properties of fracture networks from
two-dimensional observations in rocks approaching failure under triaxial
compression: Implications for fluid flow
Abstract
Approximating the three-dimensional structure of a fault network at
depth in the subsurface is key for robust estimates of fluid flow.
However, only observations of two-dimensional outcrops are often
available. To shed light on the relationship between two- and
three-dimensional measurements of fracture networks, we examine data
from a unique set of eleven X-ray synchrotron triaxial compression
experiments that reveal the evolving three-dimensional fracture network
throughout loading. Using machine learning, we derive relationships
between the two- and three-dimensional measurements of three properties
that control fluid flow: the porosity, and volume and tortuosity of the
largest fracture at a particular differential stress step. The models
predict the porosity and volume of the largest fracture with
R2 scores of >0.99, but predict
the tortuosity with maximum R2 scores of 0.68.
To test the assumption that different rock types may require different
equations between the two- and three-dimensional properties, we develop
models for both individual rock types (granite, monzonite, marble,
sandstone) and all of the experiments. Models developed using all of the
experiments perform better than models developed for individual rock
types, suggesting fundamental similarities between fracture networks in
rocks often analyzed separately. Models developed with several parallel
two-dimensional observations perform similarly to models developed with
several perpendicular two-dimensional observations. When the models are
developed with statistics of the two-dimensional observations, the
models primarily depend on the mean and median when they predict the
porosity, and minimum when they predict the volume and tortuosity.