Lithological Controls on the Permeability of Geologic Faults: Surrogate Modeling and Sensitivity Analysis
This work addresses uncertainty in subsurface flow modeling for geology and hydrology, but it is incremental as it applies an existing surrogate method to a specific domain problem.
The study tackled the computational bottleneck in analyzing fault permeability by developing a neural network surrogate model to replace expensive flow-based upscaling, enabling global sensitivity analysis that revealed dominant parameters and nonlinear interactions in lithological controls.
Fault zones exhibit complex and heterogeneous permeability structures influenced by stratigraphic, compositional, and structural factors, making them critical yet uncertain components in subsurface flow modeling. In this study, we investigate how lithological controls influence fault permeability using the PREDICT framework: a probabilistic workflow that couples stochastic fault geometry generation, physically constrained material placement, and flow-based upscaling. The flow-based upscaling step, however, is a very computationally expensive component of the workflow and presents a major bottleneck that makes global sensitivity analysis (GSA) intractable, as it requires millions of model evaluations. To overcome this challenge, we develop a neural network surrogate to emulate the flow-based upscaling step. This surrogate model dramatically reduces the computational cost while maintaining high accuracy, thereby making GSA feasible. The surrogate-model-enabled GSA reveals new insights into the effects of lithological controls on fault permeability. In addition to identifying dominant parameters and negligible ones, the analysis uncovers significant nonlinear interactions between parameters that cannot be captured by traditional local sensitivity methods.