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Refined understanding of groundwater in heterogeneous units through identification of vertical stratification of hydrochemistry as identified in the Walloon subgroup of Australia’s Great Artesian Basin

  • D. D. R. Owen
  • St. J. HerbertEmail author
Paper
  • 22 Downloads

Abstract

Groundwater management in the Great Artesian Basin (Australia) is challenging due to the large areas and heterogeneity of sequences involved. This challenge has often been addressed with up-scaled conceptual models; however, where there is significant heterogeneity or local competing groundwater uses, more detail is required. When good understanding of heterogeneity within sequences is not available, the upscaled approach can lead to a disparity between model predictions based on homogenisation inherent in upscaling and the observed behaviour of heterogeneous systems, leading to bias in results. This requires improved conceptual resolution of these heterogeneous systems. Geological data are often insufficient to achieve the required resolution. This paper reports on the use of hydrochemical data from existing bores in the Walloon subgroup of the Surat Basin, using compositional data analysis techniques to refine the unit description. The approach moves beyond simple hydrochemical characterisations of water type via conventional hydrochemical interpretations to use a combination of centred- and isometric-log ratios to describe discrete end members. The results show that, despite similar water types, the upper Juandah and lower Taroom coal seams of Walloon subgroup are distinguished by an inverse relationship between ions Cl, Ca2+, Mg2+, Sr2+, Li and H+ and ions HCO3, F and B3+, and by an isometric log ratio that describes the balance between the activities of Cl, H+ and HCO3. These relationships suggest that higher-resolution conceptual models based on flow paths mapped from hydrochemical data can form the basis for refined groundwater models that address both heterogeneity and regional model domains.

Keywords

Australia Hydrochemistry Coal seam gas Compositional data analysis Heterogeneity Isometric-log ratio 

Raffinement de la compréhension des eaux souterraines dans des unités hétérogènes grâce à l’identification de la stratification verticale de l’hydrochimie identifiée dans le sous-groupe Walloon du Grand Bassin Artésien en Australie

Résumé

La gestion des eaux souterraines dans le Grand Bassin Artésien (Australie) est difficile en raison des vastes zones et de l’hétérogénéité des séquences impliquées. Ce défi a souvent été adressé avec des modèles conceptuels mis à l’échelle. Cependant, lorsqu’il y a une hétérogénéité importante ou des utilisations locales concurrentes des eaux souterraines, plus de détails sont nécessaires. Quand une bonne compréhension de l’hétérogénéité au sein de séries est notre disponible, l’approche de changement d’échelle peut conduire à une disparité entre les prévisions du modèle basées sur une homogénéisation inhérente à la mise à l’échelle et l’observation du comportement des systèmes hétérogènes, conduisant à des biais dans les résultats. Cela nécessite une meilleure résolution conceptuelle de ces systèmes hétérogènes. Les données géologiques sont souvent insuffisantes pour atteindre la résolution requise. Cet article rend compte de l’utilisation des données hydrochimiques provenant des forages existants dans le sous-groupe Walloon du bassin de Surat, en utilisant des techniques d’analyse des données de composition pour affiner la description de l’unité. L’approche va au-delà des simples caractérisations hydrochimiques du type d’eau par des interprétations hydrochimiques conventionnelles pour utiliser une combinaison de rapports centrés et isométriques en échelle logarithmique pour décrire les différents pôles. Les résultats montrent que, malgré des types d’eau similaires, les veines de charbon du Juandah supérieur et du Taroom inférieur du sous-groupe de Walloon se différencient selon une relation inverse entre les ions Cl, Ca2+, Mg2+, Sr2+, Li et H+ et les ions HCO3, F et B3+, et par le rapport isométrique en échelle logarithmique qui décrit l’équilibre entre les activités du Cl, H+ et HCO3. Ces relations suggèrent que les modèles conceptuels à haute résolution basés sur les voies d’écoulements cartographiés à partir des données hydrogéochimiuqes peuvent constituer une base pour les modèles hydrogéologiques raffinés qui traitent à la fois de l’hétérogénéité et des domaines de modèles régionaux.

Conocimiento detallado de las aguas subterráneas en unidades heterogéneas mediante la identificación de la estratificación vertical de la hidroquímica en el Walloon subgroup de la Great Artesian Basin

Resumen

La gestión de las aguas subterráneas en la Great Artesian Basin (Australia) es un desafío debido a las grandes áreas y a la heterogeneidad de las secuencias involucradas. Este desafío se ha abordado a menudo con modelos conceptuales regionales. Sin embargo, cuando existe una heterogeneidad significativa o usos locales de las aguas subterráneas que compiten entre sí, se requieren más detalles. Cuando no se dispone de una buena comprensión de la heterogeneidad dentro de las secuencias, el enfoque regional puede dar lugar a una disparidad entre las predicciones de los modelos basados en la homogeneización inherente a la escala y el comportamiento observado de los sistemas heterogéneos, lo que conduce a un sesgo en los resultados. Esto requiere una mejor resolución conceptual de estos sistemas heterogéneos. Los datos geológicos son a menudo insuficientes para lograr la resolución requerida. Este trabajo informa sobre el uso de datos hidroquímicos de perforaciones existentes en el Walloon subgroup de la Surat Basin, utilizando técnicas de análisis de datos de composición para refinar la descripción de la unidad. El enfoque va más allá de las simples caracterizaciones hidroquímicas del tipo de agua a través de las interpretaciones hidroquímicas convencionales para utilizar una combinación de relaciones de registro centradas e isométricas para describir los miembros finales discretos. Los resultados muestran que, a pesar de tipos de agua similares, los mantos de carbón de la parte superior de Juandah y de la parte inferior de Taroom del Walloon subgroup se distinguen por una relación inversa entre los iones Cl, Ca2+, Mg2+, Sr2+, Li y H+ y los iones HCO3, F y B3+, y por una relación de registro isométrico que describe el equilibrio entre las actividades de Cl, H+ y HCO3. Estas relaciones sugieren que los modelos conceptuales de mayor resolución basados en trayectorias de flujo trazadas a partir de datos hidroquímicos pueden constituir la base de modelos detallados de aguas subterráneas que abordan tanto la heterogeneidad como los dominios de los modelos regionales.

通过识别水化学垂向分层为对应的澳大利亚大自流盆地Walloon群的非均质单元地下水的深入认识

摘要

由于大自流盆地(澳大利亚)涉及的区域大和层序非均质性, 其地下水管理具有挑战性。这些问题通常通过升尺度的概念模型来解决的。但是, 当存在大量非均质性或当地竞争性地下水开发利用的地方, 需要更多细节研究。如果无法很好地了解层序非均质性, 则升尺度的方法可能会导致基于升尺度的均质化模型预测与观察到的非均质系统行为之间出现差异, 从而导致结果出现偏差。这就要求提高非均质系​​统概念化的分辨率。地质数据通常不足以实现所需的分辨率。本文报告了利用Surat盆地Walloon群已有钻孔的水化学数据, 并使用组分数据分析技术完善了单元描述。该方法超越了通过常规水化学解释对水类型进行简单的水化学表征, 而是使用中心格式的对数比率组合来描述离散的终端组分。结果表明, Walloon 群Juandah上部和Taroom煤层下层虽然水化学类型相似, 但其Cl 、Ca2+ 、Mg2+ 、Sr2+ 、 Li 和 H+ 、 HCO3离子、F 、 B3+呈反比关系, 并通过对数比率来描述Cl, H+和HCO3活度的平衡。这些关系表明, 基于水化学数据映射的流动路径的更高分辨率的概念模型可为非均质性和区域模型区域精细化的地下水模型提供基础。

Conhecimento refinado das águas subterrâneas em unidades heterogêneas através da identificação da estratificação hidroquímica vertical, conforme identificado no subgrupo Walloon da Grande Bacia Artesiana da Austrália

Resumo

O gerenciamento das águas subterrâneas na Grande Bacia Artesiana (Austrália) é desafiador devido às grandes áreas e à heterogeneidade das sequências envolvidas. Esse desafio costuma ser enfrentado com modelos conceituais de aumento de escala. No entanto, onde há heterogeneidade significativa ou usos concorrentes de água subterrânea local, são necessários mais detalhes. Quando não há um bom entendimento da heterogeneidade das sequências, a abordagem de aumento de escala pode levar a uma disparidade entre as previsões do modelo com base na homogeneização inerente ao aumento de escala e no comportamento observado de sistemas heterogêneos, levando a vieses nos resultados. Isso requer uma resolução conceitual aprimorada desses sistemas heterogêneos. Os dados geológicos geralmente são insuficientes para alcançar a resolução necessária. Este artigo relata o uso de dados hidroquímicos de perfurações existentes no subgrupo Walloon da Bacia Surat, usando técnicas de análise de dados composicionais para refinar a descrição da unidade. A abordagem vai além das simples caracterizações hidroquímicas de tipo de água através de interpretações hidroquímicas convencionais, para usar uma combinação de razões logarítmicas centradas e isométricas para descrever membros finais discretos. Os resultados mostram que, apesar dos tipos de água semelhantes, os depósitos de carvão superiores de Juandah e inferiores de Taroom do subgrupo Walloon são distinguidos por uma relação inversa entre os íons Cl, Ca2+, Mg2+, Sr2 +, Li e H+ e os íons HCO3, F e B3 +, e por uma razão logarítmica isométrica que descreve o equilíbrio entre as atividades de Cl, H+ e HCO3. Essas relações sugerem que modelos conceituais de alta resolução baseados em caminhos de fluxo mapeados a partir de dados hidroquímicos podem formar a base para modelos refinados de água subterrânea que abordam tanto a heterogeneidade quanto os domínios de modelos regionais.

References

  1. Aitchison J (2003) The statistical analysis of compositional data. Blackburn, Caldwell, NJGoogle Scholar
  2. Appelo CAJ, Postma D (2010) Geochemistry, groundwater and pollution, 2nd edn. CRC, AmsterdamGoogle Scholar
  3. Baublys KA, Hamilton SK, Golding SD, Vink S, Esterle J (2015) Microbial controls on the origin and evolution of coal seam gases and production waters of the Walloon subgroup: Surat Basin, Australia. Int J Coal Geol 147–148:85–104.  https://doi.org/10.1016/j.coal.2015.06.007 CrossRefGoogle Scholar
  4. Buccianti A (2011a) Isometric log-ratio co-ordinates and their simple use in water geochemistry. Bol Geol Miner 122:435–458Google Scholar
  5. Buccianti A (2011b) Natural laws governing the distribution of the elements in geochemistry: the role of the log-ratio approach. In: Compositional data analysis. Wiley, LondonCrossRefGoogle Scholar
  6. Buccianti A (2013) Is compositional data analysis a way to see beyond the illusion? Comput Geosci 50:165–173CrossRefGoogle Scholar
  7. Buccianti A, Pawlowsky-Glahn V (2005) New perspectives on water chemistry and compositional data analysis. Math Geol 37:703–727CrossRefGoogle Scholar
  8. Comas-Cufí M, Thió-Henestrosa S (2011) CoDaPack 2.0: a stand-alone, multi-platform compositional software. In: Egozcue JJ, Tolosana-Delgado R, Ortego MI (eds) CoDaWork’11: 4th International Workshop on Compositional Data Analysis, Sant Feliu de Guíxols, Spain, May 2011Google Scholar
  9. Egozcue JJ, Pawlowsky-Glahn V, Mateu-Figueras G, Barceló-Vidal C (2003) Isometric logratio transformations for compositional data analysis. Math Geol 35:279–300CrossRefGoogle Scholar
  10. Firth D (1993) Bias reduction of maximum likelihood estimates. Biometrika 80:27–38CrossRefGoogle Scholar
  11. GABCC (2014) Great Artesian Basin Resource Study 2014. Great Artesian Basin Coordinating Committee. http://www.gabcc.gov.au/sitecollectionimages/resources/66540f98-c828-4268-8b8b-b37f8193cde7/files/resource-study-2016.pdf. Accessed 2 October 2018
  12. Gianni G, Doherty J, Brunner P (2018) Conceptualisation and calibration of anisotropic alluvial systems: pitfalls and biases. Groundwater.  https://doi.org/10.1111/gwat.12802 CrossRefGoogle Scholar
  13. Habermehl MA (2019) Review: the evolving understanding of the Great Artesian Basin (Australia), from discovery to current hydrogeological interpretations. Hydrogeol J.  https://doi.org/10.1007/s10040-019-02036-6
  14. Hodgkinson J, Grigorescu M (2012) Background research for selection of potential geostorage targets: case studies from the Surat Basin, Queensland. Aust J Earth Sci.  https://doi.org/10.1080/08120099.2012.662913 CrossRefGoogle Scholar
  15. Kernodle JM (1996) Hydrogeology and steady-state simulation of ground-water flow in the San Juan Basin, New Mexico, Colorado, Arizona and Utah. US Geol Surv Water Resour Invest Rep 95–4187Google Scholar
  16. King AC, Raiber M, Cox ME (2013) Multivariate statistical analysis of hydrochemical data to assess alluvial aquifer–stream connectivity during drought and flood: Cressbrook Creek, Southeast Queensland, Australia. Hydrogeol J 22:481–500CrossRefGoogle Scholar
  17. Lin A (2016) R data analysis examples: logit regression. http://www.ats.ucla.edu/stat/r/dae/logit.htm. Accessed 18 January 2017
  18. Moya CE, Raiber M, Taulis M, Cox ME (2015) Hydrochemical evolution and groundwater flow processes in the Galilee and Eromanga basins, Great Artesian Basin, Australia: a multivariate statistical approach. Sci Total Environ 508:411–426CrossRefGoogle Scholar
  19. OGIA (2016) Underground Water Impact Report for the Surat Cumulative Management Area 2016, Office of Groundwater Impact Assessment. https://www.dnrm.qld.gov.au/__data/assets/pdf_file/0007/345616/uwir-surat-basin-2016.pdf. Accessed 2 October 2018
  20. Owen DDR, Cox ME (2015) Hydrochemical evolution within a large alluvial groundwater resource overlying a shallow coal seam gas reservoir. Sci Total Environ 523:233–252CrossRefGoogle Scholar
  21. Owen DDR, Raiber M, Cox ME (2015) Relationships between major ions in coal seam gas groundwaters: examples from the Surat and Clarence-Moreton basins. Int J Coal Geol 137:77–91CrossRefGoogle Scholar
  22. Owen DDR, Shouakar-Stash O, Morgenstern U, Aravena R (2016a) Thermodynamic and hydrochemical controls on CH4 in a coal seam gas and overlying alluvial aquifer: new insights into CH4 origins. Sci Rep 6:32407.  https://doi.org/10.1038/srep32407 CrossRefGoogle Scholar
  23. Owen DDR, Pawlowsky-Glahn V, Egozcue JJ, Buccianti A, Bradd JM (2016b) Compositional data analysis as a robust tool to delineate hydrochemical facies within and between gas-bearing aquifers. Water Resour Res 52:5771–5793CrossRefGoogle Scholar
  24. Pawlowsky-Glahn V, Buccianti A (2002) Visualization and modeling of sub-populations of compositional data: statistical methods illustrated by means of geochemical data from fumarolic fluids. Int J Earth Sci 91:357–368CrossRefGoogle Scholar
  25. Pawlowsky-Glahn V, Egozcue JJ (2006) Compositional data and their analysis: an introduction. In: Buccianti A, Mateu-Figueras G, Pawlowsky-Glahn V (eds) Compositional data analysis in the geosciences: from theory to practice. Special Publications, The Geological Society of London, LondonCrossRefGoogle Scholar
  26. Pawlowsky-Glahn V, Egozcue JJ (2011) Exploring compositional data with the CoDa-Dendrogram. Aust J Stat 40(1 & 2):103–113Google Scholar
  27. QWC (2012) Underground water impact report for the Surat Cumulative Management Area 2012. Queensland Water Commission. https://www.dnrm.qld.gov.au/__data/assets/pdf_file/0016/31327/underground-water-impact-report.pdf. Accessed 2 October 2018
  28. Raiber M, Cox M (2012) Linking three-dimensional geological modelling and multivariate statistical analysis to define groundwater chemistry baseline and identify inter-aquifer connectivity within the Clarence-Moreton Basin, Southeast Queensland, Australia. In: Mares, Tennille (ed) Eastern Australasian Basins Symposium IV. Petroleum Exploration Society of Australia, Petroleum Exploration Society of Australia, Brisbane, QLD, pp 1–6Google Scholar
  29. Raiber M, White PA, Daughney CJ, Tschritter C, Davidson P, Bainbridge SE (2012) Three-dimensional geological modelling and multivariate statistical analysis of water chemistry data to analyse and visualise aquifer structure and groundwater composition in the Wairau plain, Marlborough District, New Zealand. J Hydrol 436–437:13–34CrossRefGoogle Scholar
  30. Ransley T, Somerville P, Tan KP, Feitz A, Cook S, Yates G, Schoning G, Bell J, Caruana L, Sundaram B, Wallace L (2015) Groundwater hydrochemical characterisation of the Surat Region and Laura Basin – Queensland. Geoscience Australia record 2015/05, Geoscience Australia, CanberraGoogle Scholar
  31. Shields DJ (2017) An Investigation of numerical facies models for coal seam gas reservoirs: Walloon subgroup, Surat Basin. PhD Thesis, University of Queensland, Australia.  https://doi.org/10.14264/uql.2018.55. Accessed 15 October 2018
  32. Sing T, Sander O, Beerenwinkel N, Lengauer T (2005) ROCR: visualizing classifier performance in R. Bioinformatics 21:3940–3941.  https://doi.org/10.1093/bioinformatics/bti623 CrossRefGoogle Scholar
  33. Summers P (2017) Powder River Basin coal review 2017 PRD groundwater model addendum to the 2014 Coal Review Task 1B report: current water resources conditions (as of 2010). Bureau of Land Management WY. https://eplanning.blm.gov/epl-front-office/projects/nepa/64842/150650/184866/2017PRB_Groundwater_Model-CurrentWaterResourceConditions_2014_Task_1B_Report-Addendum.pdf. Accessed November 2019
  34. Underschultz J, Vink S (2015) Emerging complexity of the GAB aquifer systems in the Surat Basin. https://ccsg.centre.uq.edu.au/files/994/UnderschultzVinkAAPGICEEABSMondayfinal.pdf. Accessed 2 October 2018

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2020

Authors and Affiliations

  1. 1.Natural Resource Research Alliance (NRRA)BrisbaneAustralia
  2. 2.Arrow EnergyBrisbaneAustralia

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