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Effects of variable-density flow on the value-of-information of pressure and concentration data for aquifer characterization

机译:变密度流量对含水层表征压力和浓度数据信息值的影响

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摘要

Predicting variable-density flow and transport in aquifers is critical for the management of many coastal saline aquifers. Accurate characterization of hydrogeological parameters is critical for prediction, and the characterization is often conducted by assimilating data into models. However, few studies have investigated the underlying physics controlling the value-of-information (VOI) of data for aquifer characterization. In this study, we show how a greater understanding of the underlying physics controlling pressure and concentration data coupling can lead to improved characterization. In variable-density flow, the key physics that controls the VOI of pressure and concentration data is the non-linear coupling between flow and transport via fluid density which causes the pressure field to experience transient changes according to the evolution of salinity distribution. We first demonstrate the coupling between pressure and concentration data using information theory, and then systematically investigate how the variable-density flow impacts the VOI of these data in relation to permeability estimation. Using an ensemble Kalman filter, we estimate the permeability field of saline aquifer systems in two scenarios of data usage: pressure data only, and pressure and concentration data jointly. This study demonstrates that, regardless of the data usage scenario, the maximum VOI of data is obtained when free convection and forced convection are balanced. We further show that the advantage of joint inversion of pressure and concentration data decreases as the coupling effect between flow and transport increases. Finally, we study how the level of permeability field heterogeneity affects the coupling, which in turn controls VOI of pressure and concentration data.
机译:预测含水层中的可变密度流量和运移对于许多沿海咸水含水层的管理至关重要。水文地质参数的准确表征对于预测至关重要,而表征通常是通过将数据同化为模型来进行的。但是,很少有研究调查用于控制含水层特征的数据信息值(VOI)的基础物理学。在这项研究中,我们展示了如何更好地理解控制压力和浓度数据耦合的基本物理原理可以如何改善表征。在变密度流中,控制压力和浓度数据的VOI的关键物理是流体之间的非线性耦合,并通过流体密度传输,这导致压力场根据盐分分布的变化而经历瞬态变化。我们首先使用信息论证论证压力和浓度数据之间的耦合,然后系统地研究可变密度流如何影响这些数据与渗透率估算有关的VOI。使用集成卡尔曼滤波器,我们在两种数据使用情况下估算盐水含水层系统的渗透率场:仅压力数据,以及压力和浓度数据。这项研究表明,无论数据使用情况如何,当自由对流和强制对流达到平衡时,可获得最大数据VOI。我们进一步表明,压力和浓度数据联合反演的优势随着流量和输运之间的耦合效应的增加而降低。最后,我们研究了渗透率场异质性水平如何影响耦合,进而控制了压力和浓度数据的VOI。

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