TY - JOUR
T1 - Equivalence of Discrete Fracture Network and Porous Media Models by Hydraulic Tomography
AU - Dong, Yanhui
AU - Fu, Yunmei
AU - Yeh, Tian Chyi Jim
AU - Wang, Yu Li
AU - Zha, Yuanyuan
AU - Wang, Liheng
AU - Hao, Yonghong
N1 - Funding Information:
This research is financially supported by the National Science and Technology Major Project of China (Grant 2017ZX05008‐003‐021). Partial support was also provided by the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant XDB10030601) and the Youth Innovation Promotion Association of the Chinese Academy of Sciences (Grant 2016063). T.‐C. Jim Yeh also acknowledges supports from US Civilain Research and Development Foundation (CRDF) under the award number (DAA2‐15‐61224‐1): Hydraulic tomography in shallow alluvial sediments: Nile River Valley, Egypt. He also acknowledges the Global Expert award through Tianjin Normal University from the Thousand Talents Plan of Tianjin City. Finally, we wish to thank Golder Associates Inc. for the support of using the FracMan software. The authors thank the editors and anonymous reviewers for their helpful and insightful comments, which have significantly improved this work. The program and the data used in this study can be achieved online (doi: 10.5281/ zenodo.2544573).
Publisher Copyright:
©2019. American Geophysical Union. All Rights Reserved.
PY - 2019/4
Y1 - 2019/4
N2 - Hydraulic 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 (Ss) 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.
AB - Hydraulic 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 (Ss) 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.
KW - discrete fracture network
KW - hydraulic tomography
KW - representative elementary volume
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U2 - 10.1029/2018WR024290
DO - 10.1029/2018WR024290
M3 - Article
AN - SCOPUS:85064638738
VL - 55
SP - 3234
EP - 3247
JO - Water Resources Research
JF - Water Resources Research
SN - 0043-1397
IS - 4
ER -