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Density data of the mixed solutions at temperatures other than ambient are unique and close existing data gaps. The numerical model reproduces the newly measured and already existing density data within an error band of approximately 1%. For further use in geothermal applications, this can be considered an excellent agreement. Moreover, the model yields a direct calculation of density without the need to establish complex empirical equations of state and mixing rules. Finally, sensitivity calculations performed with a thermal–hydraulic (TH) numerical reservoir model demonstrate the required accuracy of fluid density for reliably predicting the long-term performance of deep geothermal energy systems. 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引用次数: 0
摘要
本研究报告了新近获得的合成制备的纯净和混合氯化钠和氯化钙水溶液的密度数据,这些溶液的浓度和混合比在地热作用下的范围很广。提供的分析数据是在环境压力下的 293-353 K 温度范围内的数据。为了再现这些数据,使用了 PHREESCALE。该数值工具对已知成分的地热流体密度的预测潜力是本文的主要目标。因此,测量数据与之前文献中的分析研究结果非常吻合。本文讨论了可能的误差来源。非环境温度下混合溶液的密度数据是独一无二的,填补了现有数据空白。数值模型再现了新测量的和已有的密度数据,误差范围约为 1%。对于地热应用的进一步使用,这可以被视为极佳的一致性。此外,该模型可直接计算密度,而无需建立复杂的经验状态方程和混合规则。最后,利用热-水力(TH)数值储层模型进行的敏感性计算表明,流体密度的精确性是可靠预测深层地热能源系统长期性能所必需的。就生产力指数和热突破时间而言,它表明目前密度的分析和数值不确定性很小,足以可靠地说明这两个储层参数。
Density of pure and mixed NaCl and CaCl2 aqueous solutions at 293 K to 353 K and 0.1 MPa: an integrated comparison of analytical and numerical data
This study reports on newly acquired density data of synthetically prepared pure and mixed NaCl and CaCl2 aqueous solutions that span a wide range of geothermally encountered concentrations and mixing ratios. The analytical data are provided for the temperature range of 293–353 K at ambient pressure. For the reproduction of that data, PHREESCALE was used. The predictive potential of this numerical tool regarding the density of geothermal fluids of known composition was the major target herein. As a result, the measured data are in good agreement with previous analytical studies found in the literature. Possible sources of errors are discussed in this paper. Density data of the mixed solutions at temperatures other than ambient are unique and close existing data gaps. The numerical model reproduces the newly measured and already existing density data within an error band of approximately 1%. For further use in geothermal applications, this can be considered an excellent agreement. Moreover, the model yields a direct calculation of density without the need to establish complex empirical equations of state and mixing rules. Finally, sensitivity calculations performed with a thermal–hydraulic (TH) numerical reservoir model demonstrate the required accuracy of fluid density for reliably predicting the long-term performance of deep geothermal energy systems. In terms of the productivity index and the timing of thermal breakthrough it shows that the present analytical and numerical uncertainty in density is small enough to reliably state both reservoir parameters.
Geothermal EnergyEarth and Planetary Sciences-Geotechnical Engineering and Engineering Geology
CiteScore
5.90
自引率
7.10%
发文量
25
审稿时长
8 weeks
期刊介绍:
Geothermal Energy is a peer-reviewed fully open access journal published under the SpringerOpen brand. It focuses on fundamental and applied research needed to deploy technologies for developing and integrating geothermal energy as one key element in the future energy portfolio. Contributions include geological, geophysical, and geochemical studies; exploration of geothermal fields; reservoir characterization and modeling; development of productivity-enhancing methods; and approaches to achieve robust and economic plant operation. Geothermal Energy serves to examine the interaction of individual system components while taking the whole process into account, from the development of the reservoir to the economic provision of geothermal energy.