Determination of anisotropy of spatial correlation structure in a three‐dimensional permeability field accompanied by shallow faults

Nakaya, Shinji; Yohmei, Taiyoh; Koike, Akihisa; Hirayama, Tetsuhiro; Yoden, Toshiaki; Nishigaki, Makoto

doi:10.1029/2001wr000672

Cited by 9 publications

(7 citation statements)

References 26 publications

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“…The conduit effects of faults may only appear after geostatistical analysis (Nakaya et al . ). In large underground mines, counting the fault conduits over areas of a few square kilometers is also problematic.…”

Section: Discussionmentioning

confidence: 97%

The biases and trends in fault zone hydrogeology conceptual models: global compilation and categorical data analysis

2016

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To investigate the biases and trends in observations of the permeability structures of fault zones in various geoscience disciplines, we review and compile a database of published studies and reports containing more than 900 references. The global data are categorized, mapped, and described statistically. We use the chi‐square test for the dependency of categorical variables to show that the simplified fault permeability structure (barrier, conduit, barrier–conduit) depends on the observation method, geoscience discipline, and lithology. In the crystalline rocks, the in situ test methods (boreholes or tunnels) favor the detection of permeable fault conduits, in contrast to the outcrop‐based measurements that favor a combined barrier–conduit conceptual models. These differences also occur, to a lesser extent, in sedimentary rocks. We provide an estimate of the occurrence of fault conduits and barriers in the brittle crust. Faults behave as conduits at 70% of sites, regardless of their barrier behavior that may also occur. Faults behave as barriers at at least 50% of the sites, in addition to often being conduits. Our review of published data from long tunnels suggests that in crystalline rocks, 40–80% (median about 60%) of faults are highly permeable conduits, and 30–70% in sedimentary rocks. The trends with depth are not clear, but there are less fault conduits counted in tunnels at the shallowest depths. The barrier hydraulic behavior of faults is more uncertain and difficult to observe than the conduit.

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Section: Discussionmentioning

confidence: 97%

The biases and trends in fault zone hydrogeology conceptual models: global compilation and categorical data analysis

2016

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“…Heterogeneity and anisotropy can result in channeling of flow paths and even in retardation of mixing [e.g., Bernabé and Bruderer , 1998; Bruderer‐Weng et al , 2004; Johnson et al , 2006]. At field scale, stratification in layers more or less permeable [e.g., Chandler et al , 1989], clayey minerals in shear zones inducing alignment of grains [ Arch and Maltman , 1990; Faulkner and Rutter , 1998; Zhang and Tullis , 1998; Zhang et al , 2001; Takahashi , 2003], and preferential alignment of fractures and faults [ Zhang et al , 1996; Nakaya et al , 2002; Min et al , 2004] are sources of regional anisotropy.…”

Section: Introductionmentioning

confidence: 99%

Permeability anisotropy and its relations with porous medium structure

et al. 2008

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[1] The complete permeability tensor of 18 porous rock cores was determined by means of X-ray tomography monitoring during the displacement of a salty tracer. To study the effect of the pore space geometry on the anisotropy of permeability, we compared the three-dimensional shape of the invasion front with the X-ray porosity maps obtained before injection. The samples (clean and shale-bearing sandstones, limestones, and volcanic rocks) belong to a broad variety of granulometry and pore space geometry. Their porosity ranges from 12 to 57%, and their permeability ranges from 1.5 Â 10 À14 to 4 Â 10 À12 m 2 . For sandstones the permeability anisotropy is well correlated with the presence of bedding. For volcanic rocks it is clearly related to the orientation of vesicles or cracks. However, for limestones, no evident link between the geometry of the porous network and the permeability anisotropy appears, probably because of the influence of the nonconnected porosity that does not contribute to the hydraulic transport. This systematic work evidences the ability and the limitations of the tracer method to characterize the anisotropy of permeability in the laboratory in a simple and rapid way.Citation: Clavaud, J.-B., A. Maineult, M. Zamora, P. Rasolofosaon, and C. Schlitter (2008), Permeability anisotropy and its relations with porous medium structure,

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“…A semivariogram (or variogram) is used to analyse spatial variation structures, and kriging, a best linear unbiased estimator (BLUE), then uses measured data to estimate regional variables at unsampled locations by analysing the spatial variability. Previous research on evaluating the spatial variability and distribution of groundwater parameters includes Delhomme (1979), Hoeksema and Kitanidis (1985), Woodbury and Sudicky (1991), Hess et al (1992), Turcke and Kueper (1996), Fabbri (1997), Nakaya et al (2002) and Liu et al (2002).…”

Section: Introductionmentioning

confidence: 99%

Geostatistical analysis and conditional simulation for estimating the spatial variability of hydraulic conductivity in the Choushui River alluvial fan, Taiwan

Jang¹,

Liu²

2004

Hydrological Processes

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Abstract:This work evaluated the spatial variability and distribution of heterogeneous hydraulic conductivity (K) in the Choushui River alluvial fan in Taiwan, using ordinary kriging (OK) and mean and individual sequential Gaussian simulations (SGS). A baseline flow model constructed by upscaling parameters was inversely calibrated to determine the pumping and recharge rates. Simulated heads using different K realizations were then compared with historically measured heads. A global/local simulated error between simulated and measured heads was analysed to assess the different spatial variabilities of various estimated K distributions. The results of a MODFLOW simulation indicate that the OK realization had the smallest sum of absolute mean simulation errors (SAMSE) and the SGS realizations preserved the spatial variability of the measured K fields. Moreover, the SAMSE increases as the spatial variability of the K field increases. The OK realization yields small local simulation errors in the measured K field of moderate magnitude, whereas the SGS realizations have small local simulation errors in the measured K fields, with high and low values. The OK realization of K can be applied to perform a deterministic inverse calibration. The mean SGS method is suggested for constructing a K field when the application focuses on extreme values of estimated parameters and small calibration errors, such as in a simulation of contaminant transport in heterogeneous aquifers. The individual SGS realization is useful in stochastically assessing the spatial uncertainty of highly heterogeneous aquifers.

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Determination of anisotropy of spatial correlation structure in a three‐dimensional permeability field accompanied by shallow faults

Cited by 9 publications

References 26 publications

The biases and trends in fault zone hydrogeology conceptual models: global compilation and categorical data analysis

The biases and trends in fault zone hydrogeology conceptual models: global compilation and categorical data analysis

Permeability anisotropy and its relations with porous medium structure

Geostatistical analysis and conditional simulation for estimating the spatial variability of hydraulic conductivity in the Choushui River alluvial fan, Taiwan

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