Summary of air permeability data from single-hole injection tests in unsaturated fractured tuffs at the Apache Leap Research Site: Results of steady-state test interpretation

Guzman, Amado; Geddis, A.M.; Henrich, M.J.; Lohrstorfer, C.F.; Neuman, Shlomo P.

doi:10.2172/219309

Cited by 45 publications

(69 citation statements)

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Section: Analysis Of Log Air Permeabilities From Borehole Tests In Unmentioning

confidence: 99%

“…Our analysis focuses on log k values obtained by Guzman et al (1996) from steady state interpretations of 184 pneumatic injection tests in 1-m long intervals along 6 boreholes at the site (Fig. 1) Guzman et al (1996). Riva et al (2012) analyzed the probability distributions of 1-m scale (natural) log k measurements and their increments at the ALRS.…”

Section: Analysis Of Log Air Permeabilities From Borehole Tests In Unmentioning

confidence: 99%

“…In this paper we employ ESS to analyze the scaling behaviors of two log permeability data sets showing heavy-tailed frequency distributions in three and two spatial dimensions, respectively. One set consists of 1-m scale pneumatic packer test data from six vertical and inclined boreholes spanning a decameters-scale block of unsaturated fractured tuffs near Superior, Arizona (Guzman et al, 1996). Another set contains pneumatic minipermeameter data measured at a spacing of 15 cm along three horizontal transects on a 21 m long and 6 m high outcrop of the Upper Cretaceous Straight Cliffs Formation, including lower-shoreface bioturbated and cross-bedded sandstone near Escalante, Utah (Castle et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

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Extended power-law scaling of heavy-tailed random air-permeability fields in fractured and sedimentary rocks

Guadagnini

Riva

Neuman

2012

Hydrol. Earth Syst. Sci.

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Abstract. We analyze the scaling behaviors of two fieldscale log permeability data sets showing heavy-tailed frequency distributions in three and two spatial dimensions, respectively. One set consists of 1-m scale pneumatic packer test data from six vertical and inclined boreholes spanning a decameters scale block of unsaturated fractured tuffs near Superior, Arizona, the other of pneumatic minipermeameter data measured at a spacing of 15 cm along three horizontal transects on a 21 m long and 6 m high outcrop of the Upper Cretaceous Straight Cliffs Formation, including lowershoreface bioturbated and cross-bedded sandstone near Escalante, Utah. Order q sample structure functions of each data set scale as a power ξ(q) of separation scale or lag, s, over limited ranges of s. A procedure known as extended self-similarity (ESS) extends this range to all lags and yields a nonlinear (concave) functional relationship between ξ(q) and q. Whereas the literature tends to associate extended and nonlinear power-law scaling with multifractals or fractional Laplace motions, we have shown elsewhere that (a) ESS of data having a normal frequency distribution is theoretically consistent with (Gaussian) truncated (additive, self-affine, monofractal) fractional Brownian motion (tfBm), the latter being unique in predicting a breakdown in power-law scaling at small and large lags, and (b) nonlinear power-law scaling of data having either normal or heavy-tailed frequency distributions is consistent with samples from sub-Gaussian random fields or processes subordinated to tfBm or truncated fractional Gaussian noise (tfGn), stemming from lack of ergodicity which causes sample moments to scale differently than do their ensemble counterparts. Here we (i) demonstrate that the above two data sets are consistent with sub-Gaussian random fields subordinated to tfBm or tfGn and (ii) provide maximum likelihood estimates of parameters characterizing the corresponding Lévy stable subordinators and tfBm or tfGn functions.

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Section: Analysis Of Log Air Permeabilities From Borehole Tests In Unmentioning

confidence: 99%

Section: Analysis Of Log Air Permeabilities From Borehole Tests In Unmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Extended power-law scaling of heavy-tailed random air-permeability fields in fractured and sedimentary rocks

Guadagnini

Riva

Neuman

2012

Hydrol. Earth Syst. Sci.

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“…1 (Guzman et al 1996). Ye et al (2004) fitted seven variogram models (power P, exponential E, exponential with first-order drift E1, exponential with second order drift E2, spherical S, spherical with first-order drift S1, and spherical with second order drift S2) to this data set using the adjoint state maximum likelihood crossvalidation (ASMLCV) method of Samper and Neuman (1989) in conjunction with universal kriging and generalized least squares.…”

Section: The Apache Leap Research Sitementioning

confidence: 99%

Maximum likelihood Bayesian averaging of airflow models in unsaturated fractured tuff using Occam and variance windows

Morales-Casique

Neuman

Vesselinov

2010

Stoch Environ Res Risk Assess

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We use log permeability and porosity data obtained from single-hole pneumatic packer tests in six boreholes drilled into unsaturated fractured tuff near Superior, Arizona, to postulate, calibrate and compare five alternative variogram models (exponential, exponential with linear drift, power, truncated power based on exponential modes, and truncated power based on Gaussian modes) of these parameters based on four model selection criteria (AIC, AICc, BIC and KIC). Relying primarily on KIC and cross-validation we select the first three of these variogram models and use them to parameterize log air permeability and porosity across the site via kriging in terms of their values at selected pilot points and at some single-hole measurement locations. For each of the three variogram models we estimate log air permeabilities and porosities at the pilot points by calibrating a finite volume pressure simulator against two cross-hole pressure data sets from sixteen boreholes at the site. The traditional Occam's window approach in conjunction with AIC, AICc, BIC and KIC assigns a posterior probability of nearly 1 to the power model. A recently proposed variance window approach does the same when applied in conjunction with AIC, AICc, BIC but spreads the posterior probability more evenly among the three models when used in conjunction with KIC. We compare the abilities of individual models and MLBMA, based on both Occam and variance windows, to predict space-time pressure variations observed during two cross-hole tests other than those employed for calibration. Individual models with the largest posterior probabilities turned out to be the worst or second worst predictors of pressure in both validation cases. Some individual models predicted pressures more accurately than did MLBMA. MLBMA was far superior to any of the individual models in one validation test and second to last in the other validation test in terms of predictive coverage and log scores.

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“…Spatially distributed log air permeability data were obtained by Guzman et al (1994Guzman et al ( , 1996 (2000) fitted three variogram models to these and some 3-m-scale data using an adjoint state maximum likelihood cross-validation (ASMLCV) method developed for this purpose by Samper and Neuman (1989a,b), coupled with a generalized least squares (GLS) drift removal approach due to Neuman and Jacobson (1984). The three models included (I) power (characteristic of a random fractal), (2) exponential with a linear drift, and (3) exponential with a quadratic drift.…”

Section: Alrs Data and Previous Effortsmentioning

confidence: 99%

Combined Estimation of Hydrogeologic Conceptual Model and Parameter Uncertainty

Meyer¹,

Ye²,

Neuman³

et al. 2004

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Summary of air permeability data from single-hole injection tests in unsaturated fractured tuffs at the Apache Leap Research Site: Results of steady-state test interpretation

Abstract: Portions of this document may be illegible in electronic imrrge produced from the document.

Cited by 45 publications

References 4 publications

Extended power-law scaling of heavy-tailed random air-permeability fields in fractured and sedimentary rocks

Extended power-law scaling of heavy-tailed random air-permeability fields in fractured and sedimentary rocks

Maximum likelihood Bayesian averaging of airflow models in unsaturated fractured tuff using Occam and variance windows

Combined Estimation of Hydrogeologic Conceptual Model and Parameter Uncertainty

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