When using single‐well push‐pull (SWPP) tests to characterize in situ aquifer related to reactive transport, approximate analytical solutions were preferred to determine the aquifer dispersivity and other parameters, due to its computational efficiency. Simplified models were proposed in this study to estimate the retardation factor for a SWPP test with a reactive tracer, where the dispersivity had to be determined firstly using a SWPP test with a conservative tracer. Here, we find that previous models associated with SWPP tests contain an untested assumption when dealing with solute transport in the wellbore, probably causing great errors when interpreting the test data. This assumption states that the wellbore mixing effect is negligible in both injection and extraction phases, where the mixing effect refers to the mixing process between the native wellbore water and solute introduced into the wellbore. In this study, new approximate analytical solutions of the SWPP test are proposed for a fully penetrating well and a point‐source well by specifically considering the wellbore mixing effect. The new models of this study are tested by semianalytical solutions in Laplace domain and finite difference solutions. Results indicate that the new models perform substantially better than previous models of ignoring the wellbore mixing effect, which should not be overlooked when interpreting SWPP test data.

中文翻译：

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具有混合效应的单井推挽试验的新简化模型

当使用单井推挽（SWPP）测试来表征与反应性输运有关的原位含水层时，由于其计算效率高，因此首选近似分析解决方案来确定含水层的分散性和其他参数。在这项研究中提出了简化的模型来估计使用反应性示踪剂的SWPP测试的延迟因子，其中首先必须使用带有保守示踪剂的SWPP测试来确定分散性。在这里，我们发现与SWPP测试相关的先前模型在处理井眼中的溶质运移时包含一个未经测试的假设，这可能在解释测试数据时引起很大的错误。该假设表明，在注入和提取阶段，井眼混合效果可忽略不计，其中混合效果是指天然井眼水与引入井眼的溶质之间的混合过程。在这项研究中，通过特别考虑井眼混合效应，为全渗透井和点源井提出了新的SWPP测试近似分析解决方案。本研究的新模型通过拉普拉斯域中的半解析解和有限差分解进行了测试。结果表明，新模型在忽略井眼混合效应方面比以前的模型表现更好，这在解释SWPP测试数据时不容忽视。通过特别考虑井眼混合效应，提出了针对全渗透井和点源井的SWPP试验的新的近似分析解决方案。本研究的新模型通过拉普拉斯域中的半解析解和有限差分解进行了测试。结果表明，新模型在忽略井眼混合效应方面比以前的模型表现更好，这在解释SWPP测试数据时不容忽视。通过特别考虑井眼混合效应，提出了针对全渗透井和点源井的SWPP试验的新的近似分析解决方案。本研究的新模型通过拉普拉斯域中的半解析解和有限差分解进行了测试。结果表明，新模型在忽略井眼混合效应方面比以前的模型表现更好，这在解释SWPP测试数据时不容忽视。