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Ensemble-based stochastic permeability and flow simulation of a sparsely sampled hard-rock aquifer supported by high performance computing
Hydrogeology Journal ( IF 2.8 ) Pub Date : 2020-05-07 , DOI: 10.1007/s10040-020-02163-5
Johanna Bruckmann , Christoph Clauser

Calibrating the heterogeneous permeability distribution of hard-rock aquifers based on sparse data is challenging but crucial for obtaining meaningful groundwater flow models. This study demonstrates the applicability of stochastic sampling of the prior permeability distribution and Metropolis sampling of the posterior permeability distribution using typical production data and measurements available in the context of groundwater abstraction. The case study is the Hastenrather Graben groundwater abstraction site near Aachen, Germany. A three-dimensional numerical flow model for the Carboniferous hard-rock aquifer is presented. Monte Carlo simulations are performed, for generating 1,000 realizations of the heterogeneous hard-rock permeability field, applying Sequential Gaussian Simulation based on nine log-permeability values for the geostatistical simulation. Forward simulation of flow during a production test for each realization results in the prior ensemble of model states verified by observation data in four wells. The computationally expensive ensemble simulations were performed in parallel with the simulation code SHEMAT-Suite on the high-performance computer JURECA. Applying a Metropolis sampler based on the misfit between drawdown simulations and observations results in a posterior ensemble comprising 251 realizations. The posterior mean log-permeability is −11.67 with an uncertainty of 0.83. The corresponding average posterior uncertainty of the drawdown simulation is 1.1 m. Even though some sources of uncertainty (e.g. scenario uncertainty) remain unquantified, this study is an important step towards an entire uncertainty quantification for a sparsely sampled hard-rock aquifer. Further, it provides a real-case application of stochastic hydrogeological approaches demonstrating how to accomplish uncertainty quantification of subsurface flow models in practice.



中文翻译:

高性能计算支持的稀疏采样硬岩含水层基于集合的随机渗透率和水流模拟

基于稀疏数据校准硬岩含水层的非均质渗透率分布具有挑战性,但对于获得有意义的地下水流模型至关重要。这项研究使用典型的生产数据和在地下水抽取的背景下可获得的测量值,证明了先验渗透率分布的随机抽样和后验渗透率的Metropolis采样的适用性。案例研究是在德国亚琛附近的Hastenrather Graben地下水提取站点。提出了石炭质硬岩含水层的三维数值流模型。进行了蒙特卡洛模拟,以生成1000种非均质硬岩渗透率场的实现,应用基于九个对数渗透率值的顺序高斯模拟进行地统计模拟。对生产过程中的每个实现进行流量的正演模拟,结果得到了模型状态的预先集成,该模型状态已通过四个井中的观测数据进行了验证。在高性能计算机JURECA上,与仿真代码SHEMAT-Suite并行执行了计算量大的集成仿真。基于缩水模拟和观测值之间的不匹配应用Metropolis采样器会导致包含251个实现的后合奏。后平均对数渗透率为-11.67,不确定度为0.83。降落模拟的相应平均后验不确定度为1.1 m。即使不确定性的某些来源(例如场景不确定性)仍未量化,这项研究是对稀疏采样的硬岩含水层进行整体不确定性量化的重要一步。此外,它提供了随机水文地质方法的实际应用,展示了如何在实践中完成地下流动模型的不确定性量化。

更新日期:2020-05-07
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