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首页> 外文期刊>Applied Geochemistry: Journal of the International Association of Geochemistry and Cosmochemistry >Isotopic fingerprinting of groundwaters in southwestern Ontario: Applications to abandoned well remediation
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Isotopic fingerprinting of groundwaters in southwestern Ontario: Applications to abandoned well remediation

机译:安大略省西南部地区地下水的同位素指纹图谱:在废弃井修复中的应用

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Southwestern Ontario has a legacy of unplugged oil and gas wells drilled in the late 1800s and early 1900s before the advent of government regulatory controls. A number of these wells exhibit artesian flow of salty and/or sulphurous water at the surface, creating a liability to landowners and a possible threat to potable groundwater aquifers and the surface environment. Cost-effective plugging of these wells is hindered by incomplete drilling records regarding well depths, well construction, and geologic information on the sources of the leaking fluids. This study seeks to fill this knowledge gap by characterizing distinct geochemical 'fingerprints' for each of the major bedrock water-producing zones that may be the possible sources of the leaking fluids. To this end, over 130 groundwater samples were collected and analyzed for a broad suite of isotopic parameters (delta O-18, delta H-2, delta S-34(SO4), delta O-18(SO4), delta C-13(DIC), Sr-87/Sr-86, delta Cl-37 and delta Br-81), and this dataset was combined with available data from previous studies in the area. A Bayesian mixing model, SIAR, was applied to these data to develop a statistical tool for identifying the probable source(s) of leaking fluids. Several hypothetical samples and one real-world example are presented here to demonstrate the model's performance. (C) 2015 Elsevier Ltd. All rights reserved.
机译:安大略省西南部地区在1800年代末和1900年代初在政府监管措施出现之前就已经钻出了不插电的油气井。这些井中的许多井在地表显示咸水和/或亚硫酸的自流流动,这对地主产生了责任,并可能对饮用水的含水层和地表环境造成威胁。有关井深,井结构以及有关泄漏流体源的地质信息不完整的钻井记录,阻碍了这些井的高性价比堵塞。这项研究试图通过为每个主要基岩产水区描述不同的地球化学“指纹”来填补这一知识空白,这些可能是渗漏液的可能来源。为此,收集了130多个地下水样品并分析了一系列同位素参数(δO-18,δH-2,δS-34(SO4),δO-18(SO4),δC-13 (DIC),Sr-87 / Sr-86,δCl-37和δBr-81),并将此数据集与该地区先前研究的可用数据结合在一起。将贝叶斯混合模型SIAR应用于这些数据,以开发一种统计工具来识别泄漏流体的可能来源。这里提供了几个假设的样本和一个真实的例子来证明模型的性能。 (C)2015 Elsevier Ltd.保留所有权利。

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