Sensitivity Analysis in the Presence of Intrinsic Stochasticity for Discrete Fracture Network Simulations

Journal Article · 29 August 2024 · Journal of Geophysical Research. Machine Learning and Computation (Online)

DOI: https://doi.org/10.1029/2023JH000113 · OSTI ID:2438639

Murph, A. C.  ^[1]; Strait, J. D.  ^[1]; Moran, K. R.  ^[1]; Hyman, J. D.  ^[2]; Viswanathan, H. S.  ^[2]; Stauffer, P. H.  ^[2]

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1. Statistical Sciences (CCS‐6) Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory Los Alamos NM USA
2. Energy and Earth System Science (EES‐16) Earth and Environmental Sciences Division, Los Alamos National Laboratory Los Alamos NM USA

Abstract Large‐scale discrete fracture network (DFN) simulators are standard fare for studies involving the sub‐surface transport of particles since direct observation of real world underground fracture networks is generally infeasible. While these simulators have successfully been used in several engineering applications, estimates of output quantities of interest (QoI) — such as breakthrough time of particles reaching the edge of the system — suffer from two distinct types of uncertainty. A run of a DFN simulator requires several parameters to be set that dictate the placement and size of fractures, the density of fractures, and the overall permeability of the system; uncertainty on the proper parameters will lead to uncertainty in the QoI, called epistemic uncertainty. Furthermore, since these input settings to DFN simulators control the stochastic processes which place fractures and govern flow, understanding how this randomness affects the QoI requires several runs of the simulator at distinct random seeds. The uncertainty in the QoI attributed to different realizations (i.e., different seeds) of the same random process (i.e., identical input parameters) leads to a second type of uncertainty, called aleatoric uncertainty. In this paper, we perform a Sensitivity Analysis, which directly attributes the uncertainty observed in the QoI to the epistemic uncertainty from each input parameter and to the aleatoric uncertainty. Beyond the specific takeaways on which input variables influence uncertainty in the QoI the most, a major contribution of this paper is the introduction of a statistically rigorous workflow for characterizing the uncertainty in DFN flow simulations that exhibit heteroskedasticity.

• Sponsoring Organization: USDOE
• OSTI ID: 2438639
• Journal Information: Journal of Geophysical Research. Machine Learning and Computation (Online), Journal Name: Journal of Geophysical Research. Machine Learning and Computation (Online) Journal Issue: 3 Vol. 1; ISSN 2993-5210
• Publisher: American Geophysical Union (AGU)Copyright Statement
• Country of Publication: United States
• Language: English