Equivalence of Discrete Fracture Network and Porous Media Models by Hydraulic Tomography
AffiliationUniv Arizona, Dept Hydrol & Atmospher Sci
MetadataShow full item record
PublisherAMER GEOPHYSICAL UNION
CitationDong, Y., Fu, Y., Yeh, T.‐C. J., Wang, Y.‐L., Zha, Y., Wang, L., & Hao, Y. (2019). Equivalence of discrete fracture network and porous media models by hydraulic tomography. Water Resources Research, 55, 3234–3247. https://doi.org/10.1029/2018WR024290
JournalWATER RESOURCES RESEARCH
RightsCopyright © 2019. American Geophysical Union. All Rights Reserved.
Collection InformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at firstname.lastname@example.org.
AbstractHydraulic tomography (HT) has emerged as a potentially viable method for mapping fractures in geologic media as demonstrated by recent studies. However, most of the studies adopted equivalent porous media (EPM) models to generate and invert hydraulic interference test data for HT. While these models assign significant different hydraulic properties to fractures and matrix, they may not fully capture the discrete nature of the fractures in the rocks. As a result, HT performance may have been overrated. To explore this issue, this study employed a discrete fracture network (DFN) model to simulate hydraulic interference tests. HT with the EPM model was then applied to estimate the distributions of hydraulic conductivity (K) and specific storage (S-s) of the DFN. Afterward, the estimated fields were used to predict the observed heads from DFN models, not used in the HT analysis (i.e., validation). Additionally, this study defined the spatial representative elementary volume (REV) of the fracture connectivity probability for the entire DFN dominant. The study showed that if this spatial REV exists, the DFN is deemed equivalent to EPM and vice versa. The hydraulic properties estimated by HT with an EPM model can then predict head fields satisfactorily over the entire DFN domain with limited monitoring wells. For a sparse DFN without this spatial REV, a dense observation network is needed. Nevertheless, HT is able to capture the dominant fractures.
Note6 month embargo; published online: 23 April 2019
VersionFinal published version
SponsorsNational Science and Technology Major Project of China [2017ZX05008-003-021]; Strategic Priority Research Program of the Chinese Academy of Sciences [XDB10030601]; Youth Innovation Promotion Association of the Chinese Academy of Sciences ; US Civilain Research and Development Foundation (CRDF) under the award: Hydraulic tomography in shallow alluvial sediments: Nile River Valley, Egypt [DAA2-15-61224-1]; Global Expert award through Tianjin Normal University from the Thousand Talents Plan of Tianjin City