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HydrogeoSieveXL: an Excel-based tool to estimate hydraulic conductivity from grain-size analysis

HydrogeoSieveXL: un outil développé sous Excel pour estimer la conductivité hydraulique à parrit de l’analyse de la granulométrie

HydrogeoSieveXL: una herramienta basada en el Excel para estimar la conductividad hidráulica a partir del análisis del tamaño de grano

水文地质筛网程序XL: 一个基于Excel的通过粒径分析估算水力传导率

HydrogeoSieveXL: uma ferramenta baseada no Excel para estimar condutividade hidráulica a partir de análises do tamanho de partículas

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An Erratum to this article was published on 14 December 2016

Abstract

For over a century, hydrogeologists have estimated hydraulic conductivity (K) from grain-size distribution curves. The benefits of the practice are simplicity, cost, and a means of identifying spatial variations in K. Many techniques have been developed over the years, but all suffer from similar shortcomings: no accounting of heterogeneity within samples (i.e., aquifer structure is lost), loss of grain packing characteristics, and failure to account for the effects of overburden pressure on K. In addition, K estimates can vary by an order of magnitude between the various methods, and it is not generally possible to identify the best method for a given sample. The drawbacks are serious, but the advantages have seen the use of grain-size distribution curves for K estimation continue, often using a single selected method to estimate K in a given project. In most cases, this restriction results from convenience. It is proposed here that extending the analysis to include several methods would be beneficial since it would provide a better indication of the range of K that might apply. To overcome the convenience limitation, an Excel-based spreadsheet program, HydrogeoSieveXL, is introduced here. HydrogeoSieveXL is a freely available program that calculates K from grain-size distribution curves using 15 different methods. HydrogeoSieveXL was found to calculate K values essentially identical to those reported in the literature, using the published grain-size distribution curves.

Résumé

Depuis plus d’un siècle, les hydrogéologues ont estimé la conductivité hydraulique (K) à partir des courbes de distribution de la granulométrie. Les bénéfices de cette pratique sont la simplicité, le coût, et les moyens d’identifier les variations spatiales de K. Plusieurs techniques ont été développées au cours des années, mais toutes souffrent des raccourcis similaires : pas de prise en compte de l’hétérogénéité au sein des échantillons (par ex. la structure de l’aquifère est perdue), perte des caractéristiques de l’arrangement des grains, et manque de prise en compte des effets de la pression de charge sur K. De plus, les estimations de K peuvent varier d’un ordre de grandeur selon les différentes méthodes, et il n’est généralement pas possible d’identifier la meilleure méthode pour un exemple donné. Les inconvénients sont importants, mais les avantages ont vu l’utilisation de courbes de distribution de la granulométrie pour une estimation continue de K, souvent en utilisant une méthode unique sélectionnée pour estimer K dans un projet donné. Dans la plupart des cas, cette restriction résulte de la commodité. Il est proposé ici que l’extension de l’analyse en introduisant plusieurs méthodes serait bénéfique, car elle fournirait une meilleure indication de la gamme de K qui pourrait être appliquée. Pour surmonter la limitation de commodité, un programme développé à l’aide de feuilles de calcul sous Excel, HydrogeoSieveXL, est introduit dans cet article. HydrogeoSieveXL est un programme disponible gratuitement qui calcule K à partir des courbes de granulométrie en utilisant 15 méthodes différentes. HydrogeoSieveXL permet de calculer des valeurs de K sensiblement identiques à celles rapportées dans la littérature, en utilisant des courbes de distribution de granulométrie publiées.

Resumen

Durante más de un siglo, los hidrogeólogos estimaron la conductividad hidráulica (K) a partir de las curvas de distribución del tamaño de grano. Los beneficios de esta práctica son la simplicidad, el costo y un medio para identificar las variaciones espaciales de K. Se han desarrollado muchas técnicas a lo largo de los años, pero todas adolecen de carencias similares: no tener en cuenta la heterogeneidad dentro de las muestras (es decir, se pierde la estructura del acuífero), la pérdida de las características del empaquetamiento de granos, y la falla en considerar los efectos de la sobrepresión sobre K. Además, las estimaciones de K pueden variar en un orden de magnitud entre los distintos métodos, y generalmente no es posible identificar el mejor método para una muestra dada. Los inconvenientes son serios, pero las ventajas en el uso de curvas de distribución de tamaño de grano para las estimaciones de K continúan, a menudo utilizando un único método seleccionado para estimar K en un proyecto determinado. En la mayoría de los casos, esta restricción proviene de la conveniencia. Aquí se propone extender el análisis para incluir varios métodos lo cual sería beneficioso ya que proporcionaría una mejor indicación del rango de K que sería aplicable. Para superar la limitación de la conveniencia, se introduce aquí un programa de hoja de cálculo, HydrogeoSieveXL, basado en Excel. HydrogeoSieveXL es un programa de libre acceso que calcula K a partir de curvas de distribución de tamaño de grano utilizando 15 métodos diferentes. Se encontró que los valores de K calculados por HydrogeoSieveXL son esencialmente idénticos a los reportados en la literatura, utilizando las curvas publicadas de distribución de tamaño de grano.

摘要

一百多年来,水文地质学家都是通过粒径分布曲线估算水力传导率(K)的。这种做法的好处就是简单、成本低,也是确定k 值空间变化的一种手段。多年以来,开发了许多技术,但都有类似的缺点:没有解释样品(即:含水层结构丢失)内的非均质性,颗粒排列特征的缺失及无法解释覆盖层压力对k值的影响。另外,k估算值在不同的方法之间有数量级的变化,通常不能确定针对某一样品的最好方法。弊端非常严重,但优势就是用于估算k值的粒径分布曲线继续在使用,常常在某一项目中采用单一选择的方法估算k值。在大部分情况下,这个限制起因于便利。在此提出,拓展包含几种方法的分析将是有益的,因为这会提供更好的可能应用的k值范围。为了克服便利上的局限性,这里介绍了一种基于Excel电子指标程序—水文筛网程序XL。水文筛网程序XL是一个可免费获取的程序,采用15种不同的方法通过粒径分布曲线计算k值。发现水文筛网程序XL计算的k值和文献记载的、采用公布的粒径分布曲线得到的k值基本相同。

Resumo

Por mais de um século, hidrogeólogos têm estimado a condutividade hidráulica (K) por curvas de distribuição do tamanho de partículas. Os benefícios dessa prática são simplicidade, custo, e uma maneira de identificar variações espaciais na K. Muitas técnicas têm sido desenvolvidas ao longo dos anos, mas todas sofrem da mesma deficiência: não consideram a heterogeneidade entre amostras (por exemplo, a estrutura do aquífero é perdida), a perda de características de arranjo das partículas, e falham ao considerar os efeitos de sobrecargas de pressão na K. Além disso, estimativas de K podem variar em uma ordem de grandeza entre vários métodos, e geralmente não é possível de se identificar o melhor método para uma determinada amostra. Os inconvenientes são sérios, mas as vantagens que permitiram o uso de curvas de distribuição do tamanho de partículas para estimar K continuam, frequentemente utilizando um único método selecionado para estimar K em um determinado projeto. Na maioria dos casos, essa restrição é resultado da conveniência. É proposto aqui que a extensão das análises para que se incluam vários métodos seria benéfico, desde que isso fornecesse uma melhor indicação do intervalo de K que se ajuste à realidade. Para superar a limitação da conveniência, é aqui introduzido um programa baseado em planilha eletrônica Excel, HydrogeoSieveXL. HydrogeoSieveXL é um programa disponível de forma livre, que calcula K a partir de curvas de distribuição do tamanho de partículas utilizando 15 métodos diferentes. HydrogeoSieveXL foi instaurado para calcular valores K essencialmente idênticos àqueles descritos na literatura, utilizando as curvas de distribuição do tamanho de partículas anteriormente publicadas.

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Acknowledgements

Helpful comments provided by Mary Hill and help from Sven Fuchs identifying original citations are gratefully acknowledged.

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Correspondence to J. F. Devlin.

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An erratum to this article is available at http://dx.doi.org/10.1007/s10040-016-1512-x.

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Devlin, J.F. HydrogeoSieveXL: an Excel-based tool to estimate hydraulic conductivity from grain-size analysis. Hydrogeol J 23, 837–844 (2015). https://doi.org/10.1007/s10040-015-1255-0

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