On the electrical‐hydraulic conductivity correlation in aquifers

Purvance, David T.; Andričević, Roko

doi:10.1029/2000wr900165

Cited by 130 publications

(90 citation statements)

References 32 publications

(25 reference statements)

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“…Many authors have stated that formation factor maintains a narrow correlation with permeability (Schopper, 1966;Carothers, 1968;Sawyer et al, 1971;Ogbe and Bassiouni, 1978;Paterson, 1983;Wong et al, 1984;Purvance and Andricevic, 2000). These correlations did not attain a widespread use for any consolidated formation, which is partly due to computation of the formation factor focused on finding a single value for the cementation exponent for all layers of a specific geological formation.…”

Section: Introductionmentioning

confidence: 99%

Geophysical Estimation of Permeability in Sedimentary Media with Porosities from 0 to 50%

Díaz-Curiel

Biosca

Miguel

2015

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

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-The main objective of this study is to find a relationship between permeability and porosity, in the range 0 up to 50%, so that the permeability could be estimated only through geophysical parameters. This relationship should be consistent with the statistically expected values in sedimentary basins, and it should include the positive correlation between permeability and porosity in consolidated media and the negative correlation in unconsolidated ones. We have also developed another relationship between formation factor and permeability, considering that the values of porosity and pore fluid resistivity are known from other sources different from resistivity logs. -The method for determining these relationships has been to generate functions with the expected geometry and to fit them to empirical data drawn from published papers. As a result we obtained a new relationship between porosity and permeability, with cementation exponent as added variable, and a new relationship for indirect estimation of permeability from the formation factor. Given the formation factor dependence with clay content, we have also developed a theoretical relationship to calculate the formation factor in a clayey media, assuming that clay content is known from other logs. In this relationship, we have not taken into account the presence of non-conductive liquid phases, such as oil.Comparison of the results obtained from these expressions with data collected in the literature leads to conclude that it is possible to determine the permeability of each layer in any zone from resistivity logs, if both the clay content and the resistivity of the interstitial fluid are known.Résumé -Estimation géophysique de la perméabilité dans les milieux sédimentaires avec des porosités comprises entre 0 et 50 % -L'objectif principal de cette étude est de trouver une relation entre la perméabilité et la porosité dans la gamme de 0 à 50 %, de sorte que la perméabilité puisse être estimée seulement par des paramètres géophysiques. Cette relation doit être statistiquement compatible avec les valeurs attendues dans les bassins sédimentaires, et inclure la corrélation positive entre la perméabilité et la porosité dans les milieux consolidés et la corrélation négative dans ceux non consolidés. Nous avons également développé une autre relation entre le facteur de formation et la perméabilité, étant donné que les valeurs de la porosité et de la résistivité du fluide des pores sont connues de diverses sources autres que les diagraphies de résistivité. La méthode de détermination de ces relations a été générée en fonction de la géométrie attendue, et ajustée aux données empiriques des documents publiés. En conséquence, nous avons obtenu une nouvelle relation entre la porosité et la perméabilité, en utilisant l'exposant de cimentation comme une variable supplémentaire, et un nouveau rapport à l'estimation indirecte de la perméabilité à partir du facteur de formation. Compte tenu de la dépendance du facteur de formation avec la teneur en argile, nous avons ...

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Section: Introductionmentioning

confidence: 99%

Geophysical Estimation of Permeability in Sedimentary Media with Porosities from 0 to 50%

Díaz-Curiel

Biosca

Miguel

2015

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

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“…Both positive and negative relationships have been reported, and there can be significant uncertainty in the relationship (e.g., Mazáč et al, 1985). It is common to use a linear log-log relationship, which is given some theoretical support by Purvance and Andricevic (2000).…”

Section: Step 3 -Generation Of a Reference Geophysical System And Datmentioning

confidence: 99%

“…Using a perfect relationship to generate resistivity from hydraulic conductivity must be characterized as the ideal case because in this case electrical resistivity data can be expected to provide maximal information about hydraulic conductivity. In practice, when possible, estimation of hydraulic conductivity from electrical resistivity is usually based on a site-specific noisy linear log-log relationship (see, e.g., Mazáč et al, 1985;Revil and Cathles, 1999;Purvance and Andricevic, 2000;Slater, 2007), which has been found to be a positive relationship in some cases (Urish, 1981;Frohlich and Kelly, 1985), and a negative relationship in other cases (Worthington, 1975;Heigold et al, 1979;Biella et al, 1983).…”

Section: Reference Geophysical System and Data -Stepmentioning

confidence: 99%

“…In JHI, the hydrologic and geophysical models are coupled directly through some of their parameters using assumed relationships among the geophysical and/or hydrologic parameters (Hyndman et al, 1994). For EM data, JHI is typically done using a relationship between hydraulic conductivity and electrical resistivity inspired by Archie's law (Archie, 1942;Revil and Cathles, 1999;Purvance and Andricevic, 2000;Slater, 2007).…”

Section: Informing Hydrologic Models With Geophysicsmentioning

confidence: 99%

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Testing alternative uses of electromagnetic data to reduce the prediction error of groundwater models

Christensen¹,

Christensen²,

Ferré³

2016

Hydrol. Earth Syst. Sci.

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Abstract. In spite of geophysics being used increasingly, it is often unclear how and when the integration of geophysical data and models can best improve the construction and predictive capability of groundwater models. This paper uses a newly developed HYdrogeophysical TEst-Bench (HYTEB) that is a collection of geological, groundwater and geophysical modeling and inversion software to demonstrate alternative uses of electromagnetic (EM) data for groundwater modeling in a hydrogeological environment consisting of various types of glacial deposits with typical hydraulic conductivities and electrical resistivities covering impermeable bedrock with low resistivity (clay). The synthetic 3-D reference system is designed so that there is a perfect relationship between hydraulic conductivity and electrical resistivity. For this system it is investigated to what extent groundwater model calibration and, often more importantly, model predictions can be improved by including in the calibration process electrical resistivity estimates obtained from TEM data. In all calibration cases, the hydraulic conductivity field is highly parameterized and the estimation is stabilized by (in most cases) geophysics-based regularization.For the studied system and inversion approaches it is found that resistivities estimated by sequential hydrogeophysical inversion (SHI) or joint hydrogeophysical inversion (JHI) should be used with caution as estimators of hydraulic conductivity or as regularization means for subsequent hydrological inversion. The limited groundwater model improvement obtained by using the geophysical data probably mainly arises from the way these data are used here: the alternative inversion approaches propagate geophysical estimation errors into the hydrologic model parameters. It was expected that JHI would compensate for this, but the hydrologic data were apparently insufficient to secure such compensation. With respect to reducing model prediction error, it depends on the type of prediction whether it has value to include geophysics in a joint or sequential hydrogeophysical model calibration. It is found that all calibrated models are good predictors of hydraulic head. When the stress situation is changed from that of the hydrologic calibration data, then all models make biased predictions of head change. All calibrated models turn out to be very poor predictors of the pumping well's recharge area and groundwater age. The reason for this is that distributed recharge is parameterized as depending on estimated hydraulic conductivity of the upper model layer, which tends to be underestimated. Another important insight from our analysis is thus that either recharge should be parameterized and estimated in a different way, or other types of data should be added to better constrain the recharge estimates.

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“…The electrical resistivity (ρ), on the other hand, depends on the salinity of formation water (Putvance 2000). Therefore, the electrical conductivity (EC), which responds to the salinity of formation water, is also related to hydraulic conductivity.…”

Section: Study Areamentioning

confidence: 99%

Supervised Intelligent Committee Machine Method for Hydraulic Conductivity Estimation

Tayfur

Nadiri

Moghaddam

2014

Water Resour Manage

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Hydraulic conductivity is the essential parameter for groundwater modeling and management. Yet estimation of hydraulic conductivity in a heterogeneous aquifer is expensive and time consuming. In this study; artificial intelligence (AI) models of Sugeno Fuzzy Logic (SFL), Mamdani Fuzzy Logic (MFL), Multilayer Perceptron Neural Network associated with Levenberg-Marquardt (ANN), and Neuro-Fuzzy (NF) were applied to estimate hydraulic conductivity using hydrogeological and geoelectrical survey data obtained from Tasuj Plain Aquifer, Northwest of Iran. The results revealed that SFL and NF produced acceptable performance while ANN and MFL had poor prediciton. A supervised intelligent committee machine (SICM), which combines the results of individual AI models using a supervised artificial neural network, was developed for better prediction of the hydraulic conductivity in Tasuj plain. The performance of SICM was also compared to those of the simple averaging and weighted averaging intelligent committee machine (ICM) methods. The SICM model produced reliable estimates of hydraulic conductivity in heterogeneous aquifers.

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On the electrical‐hydraulic conductivity correlation in aquifers

Cited by 130 publications

References 32 publications

Geophysical Estimation of Permeability in Sedimentary Media with Porosities from 0 to 50%

Geophysical Estimation of Permeability in Sedimentary Media with Porosities from 0 to 50%

Testing alternative uses of electromagnetic data to reduce the prediction error of groundwater models

Supervised Intelligent Committee Machine Method for Hydraulic Conductivity Estimation

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