Felix Schölderle, Daniela Pfrang, Valerie Ernst, Theis Winter, Kai Zosseder
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To do this, we derive geophysical and hydraulic parameters from the existing borehole measurements and combine them with hydrochemical, historical and technical data to derive a set of 24 individual parameters. For this parameter set, we carry out a principal component analysis (PCA) and single linkage hierarchical cluster analysis (HCA). The PCA reduces the data set to six main factors, which explain 80% of the data set variability. Of those, the most important factors fa1, fa2, and fa3, which explain 53% of the data set variability, contain mainly geological (fa1), hydrochemical (fa2), and technical parameters (fa3). The HCA reveals four main clusters, with clusters 3 and 2 in the north, 1 in the center of the study area, and 4 in the south. The spatial location of these clusters fits very well with the zoning assumed in the previous assessment analysis ‘Masterplan Geothermal Energy‘. Cluster 2 behaves very similar to cluster 3, but is separated from it by a different hydrochemistry (fa2). In addition, two outliers were identified at two doublets in the north of the study area, which are distinguished from the main clusters in one case by differing hydrochemistry and in the other case by differing hydraulic and thermal conditions. Furthermore, a sub-cluster of cluster 4 is the only one that scatters across the entire study area. However, this can be explained by factors that do not directly influence the productivity of the boreholes concerned. 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引用次数: 0
摘要
位于巴伐利亚州的北阿尔卑斯前陆盆地是欧洲最重要的地热能利用深层储层之一。大多数工厂位于慕尼黑大都会地区,那里既有有利的地质条件,又有由于城市特征而产生的高热量需求。然而,该地区的潜力远未得到充分利用,因此在慕尼黑市和周边城市规划了广泛的地热开发。可靠的生产力预测有助于确保这种开发是可持续和高效的。我们利用该地区密集而全面的钻井数据集,使用多元方法推导出产能分区。为了做到这一点,我们从现有的井眼测量中得出地球物理和水力参数,并将它们与水化学、历史和技术数据结合起来,得出一组24个单独的参数。对于这个参数集,我们进行了主成分分析(PCA)和单链接层次聚类分析(HCA)。PCA将数据集减少到六个主要因素,这解释了80%的数据集变异性。其中,最重要的因子fa1、fa2和fa3主要包括地质(fa1)、水化学(fa2)和技术参数(fa3),它们解释了53%的数据集变异性。HCA显示了四个主要的集群,集群3和集群2在北部,1在研究区域的中心,4在南部。这些集群的空间位置与之前的“地热能总体规划”评估分析中假设的分区非常吻合。簇2的行为与簇3非常相似,但通过不同的水化学(fa2)将其分开。此外,在研究区北部的两个双重层中发现了两个异常值,其中一个是通过不同的水化学成分,另一个是通过不同的水力和热条件来区分主要集群。此外,集群4的子集群是唯一一个分散在整个研究区域的子集群。然而,这可以用不直接影响有关井眼产能的因素来解释。结果表明,从北向南可将储层划分为A ~ C 3种产能类型,并根据深度趋势回归方程推导出不同的流出温度和孔隙度。图形抽象
Productivity zoning and petrophysical assessment in the Munich metropolitan area for hydro-geothermal utilization using multivariate methods
The North Alpine Foreland Basin in Bavaria is one of Europe’s most important deep reservoirs for hydrogeothermal energy utilization for district heating. Most of the plants are located in the Munich metropolitan region, where there are both favorable geological conditions and a high demand for heat due to the urban character. However, the region's potential is far from fully utilized and extensive geothermal development is thus planned in the city of Munich and the surrounding municipalities. Reliable productivity prognoses help to ensure that this development is sustainable and efficient. We use the dense and comprehensive drilling data set in the region to derive a productivity zonation using multivariate methods. To do this, we derive geophysical and hydraulic parameters from the existing borehole measurements and combine them with hydrochemical, historical and technical data to derive a set of 24 individual parameters. For this parameter set, we carry out a principal component analysis (PCA) and single linkage hierarchical cluster analysis (HCA). The PCA reduces the data set to six main factors, which explain 80% of the data set variability. Of those, the most important factors fa1, fa2, and fa3, which explain 53% of the data set variability, contain mainly geological (fa1), hydrochemical (fa2), and technical parameters (fa3). The HCA reveals four main clusters, with clusters 3 and 2 in the north, 1 in the center of the study area, and 4 in the south. The spatial location of these clusters fits very well with the zoning assumed in the previous assessment analysis ‘Masterplan Geothermal Energy‘. Cluster 2 behaves very similar to cluster 3, but is separated from it by a different hydrochemistry (fa2). In addition, two outliers were identified at two doublets in the north of the study area, which are distinguished from the main clusters in one case by differing hydrochemistry and in the other case by differing hydraulic and thermal conditions. Furthermore, a sub-cluster of cluster 4 is the only one that scatters across the entire study area. However, this can be explained by factors that do not directly influence the productivity of the boreholes concerned. Our results indicate that we can divide the reservoir from north to south into three productivity types A to C, where we derive different outflow temperatures and porosities from regression equations of depth trends.
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.