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    Transient flow modeling in fractured media using graphs

    Shriram Srinivasan*

    Daniel O'Malley, Jeffrey D. Hyman, Satish Karra, and Hari S. Viswanathan

    Gowri Srinivasan

    • Center for Nonlinear Studies and Computational Earth Science, Earth and Environmental Sciences Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA

    • Computational Earth Science, Earth and Environmental Sciences Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA

    • X Computational Physics Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA

    • *shrirams@lanl.gov

    Phys. Rev. E 102, 052310 – Published 20 November, 2020

    DOI: https://doi.org/10.1103/PhysRevE.102.052310

    Abstract

    We describe a method to simulate transient fluid flows in fractured media using an approach based on graph theory. Our approach builds on past work where the graph-based approach was successfully used to simulate steady-state fluid flows in fractured media. We find a mean computational speedup of the order of 1400 from an ensemble of a 100 discrete fracture networks in contrast to the O(104) speedup that was obtained for steady-state flows earlier. However, the transient flows considered here involve an additional degree of complexity that was not present in the steady-state flows considered previously with a graph-based approach, that of time marching and solution of the flow equations within a time-stepping scheme. We verify our method with an analytical test case and demonstrate its use on a practical problem related to fluid flows in hydraulically fractured reservoirs. By enabling the study of transient flows, we create an opportunity for a wide set of possibilities where a steady-state approximation is not sufficient, such as the example motivated by hydraulic fracturing that we present here. This work validates the concept that graphs are able to reliably capture the topological properties of the fracture network and serve as effective surrogates in an uncertainty-quantification framework.

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