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Efficient Monte Carlo With Graph-Based Subsurface Flow and Transport Models

Journal Article · · Water Resources Research
DOI:https://doi.org/10.1029/2017WR022073· OSTI ID:1492540
 [1];  [1];  [1];  [1];  [1]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
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Simulating flow and transport in fractured porous media frequently involves solving numerical discretizations of partial differential equations with a large number of degrees of freedom using discrete fracture network (DFN) models. Uncertainty in the properties of the fracture network that controls flow and transport requires a large number of DFN simulations to statistically describe quantities of interest. However, the computational cost of solving more than a few realizations of a large DFN can be intractable. As a means of circumventing this problem, we utilize both a high-fidelity DFN model and a graphbased model of flow and transport in combination with a multifidelity Monte Carlo (MC) method to reduce the number of high-fidelity simulations that are needed to obtain an accurate estimate of the quantity of interest. We demonstrate the approach by estimating quantiles of the breakthrough time for a conservative tracer in an ensemble of fractured porous media. Our results demonstrate that a multifidelity MC estimate, whose computational cost is equal to the cost of 10 DFN simulations, can be as accurate as a standard MC estimate that utilizes 1,000 DFN simulations. Thus the combination of our graphbased model with multifidelity MC estimates effectively reduces the computational cost of the problem by a factor of approximately 100.
Research Organization:
Los Alamos National Laboratory (LANL)
Sponsoring Organization:
USDOE
Grant/Contract Number:
89233218CNA000001
OSTI ID:
1492540
Alternate ID(s):
OSTI ID: 1438943
Report Number(s):
LA-UR-18-29485
Journal Information:
Water Resources Research, Journal Name: Water Resources Research Journal Issue: 5 Vol. 54; ISSN 0043-1397
Publisher:
American Geophysical Union (AGU)Copyright Statement
Country of Publication:
United States
Language:
English

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Cited By (4)

Convergence Tests for Transdimensional Markov Chains in Geoscience Imaging journal May 2019
Advancing Graph-Based Algorithms for Predicting Flow and Transport in Fractured Rock journal September 2018
Deep Convolutional Encoder‐Decoder Networks for Uncertainty Quantification of Dynamic Multiphase Flow in Heterogeneous Media journal January 2019
Convergence Tests for Transdimensional Markov Chains in Geoscience Imaging text January 2019

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