{"title":"基于高分辨率传感器数据和低分辨率化学分析的深地热井流态变化预测","authors":"A. Dietmaier, T. Baumann","doi":"10.5194/adgeo-58-189-2023","DOIUrl":null,"url":null,"abstract":"Abstract. Geothermal waters provide a great resource to generate clean energy,\nhowever, there is a notorious lack of high quality data on these\nwaters. The scarcity of deep geothermal aquifer information is\nlargely due to inaccessibility and high analysis costs.\nHowever, multiple operators use geothermal wells in Lower Bavaria and Upper\nAustria for balneological (medical and wellness) applications as well\nas for heat mining purposes.\nThe state of the art sampling strategy budgets for a sampling frequency\nof 1 year. Previous studies have shown that robust groundwater data\nrequires sampling intervals of 1–3 months, however, these\nstudies are based on shallow aquifers which are more likely to be\ninfluenced by seasonal changes in meteorological conditions.\nThis study set out to assess whether yearly sampling adequately\nrepresents sub-yearly hydrochemical fluctuations in the aquifer by\ncomparing yearly with quasi-continuous hydrochemical data\nat two wells in southeast Germany by assessing mean, trend and\nseasonality detection among the high and low temporal resolution\ndata sets. Furthermore, the ability to produce reliable forecasts\nbased on yearly data was examined. In order to test the applicability\nof virtual sensors to elevate the information content of yearly data,\ncorrelations between the individual parameters were assessed.\nThe results of this study show that seasonal hydrochemical\nvariations take place in deep aquifers, and are not adequately\nrepresented by yearly data points, as they are typically gathered\nat similar production states of the well and do not show varying\nstates throughout the year. Forecasting on the basis of\nyearly data does not represent the data range of currently\nmeasured continuous data. The limited data availability did not\nallow for strong correlations to be determined.\nWe found that annual measurements, if taken at regular intervals and\nroughly the same production rates, represent only a snapshot of the\npossible hydrochemical compositions. Neither mean values, trends nor\nseasonality was accurately captured by yearly data. This could lead to a violation of stability criteria for mineral water, or to problems in the geothermal operation (scalings, degassing). We thus recommend\na new testing regime of at least 3 samples a year. While not a replacement for the detailed analyses, under the right circumstances, and when trained with more substantial data sets, viertual sensors provide a robust method in this setting to trigger further actions.\n","PeriodicalId":7329,"journal":{"name":"Advances in Geosciences","volume":"27 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Forecasting changes of the flow regime at deep geothermal wells based on high resolution sensor data and low resolution chemical analyses\",\"authors\":\"A. Dietmaier, T. Baumann\",\"doi\":\"10.5194/adgeo-58-189-2023\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract. Geothermal waters provide a great resource to generate clean energy,\\nhowever, there is a notorious lack of high quality data on these\\nwaters. The scarcity of deep geothermal aquifer information is\\nlargely due to inaccessibility and high analysis costs.\\nHowever, multiple operators use geothermal wells in Lower Bavaria and Upper\\nAustria for balneological (medical and wellness) applications as well\\nas for heat mining purposes.\\nThe state of the art sampling strategy budgets for a sampling frequency\\nof 1 year. Previous studies have shown that robust groundwater data\\nrequires sampling intervals of 1–3 months, however, these\\nstudies are based on shallow aquifers which are more likely to be\\ninfluenced by seasonal changes in meteorological conditions.\\nThis study set out to assess whether yearly sampling adequately\\nrepresents sub-yearly hydrochemical fluctuations in the aquifer by\\ncomparing yearly with quasi-continuous hydrochemical data\\nat two wells in southeast Germany by assessing mean, trend and\\nseasonality detection among the high and low temporal resolution\\ndata sets. Furthermore, the ability to produce reliable forecasts\\nbased on yearly data was examined. In order to test the applicability\\nof virtual sensors to elevate the information content of yearly data,\\ncorrelations between the individual parameters were assessed.\\nThe results of this study show that seasonal hydrochemical\\nvariations take place in deep aquifers, and are not adequately\\nrepresented by yearly data points, as they are typically gathered\\nat similar production states of the well and do not show varying\\nstates throughout the year. Forecasting on the basis of\\nyearly data does not represent the data range of currently\\nmeasured continuous data. The limited data availability did not\\nallow for strong correlations to be determined.\\nWe found that annual measurements, if taken at regular intervals and\\nroughly the same production rates, represent only a snapshot of the\\npossible hydrochemical compositions. Neither mean values, trends nor\\nseasonality was accurately captured by yearly data. This could lead to a violation of stability criteria for mineral water, or to problems in the geothermal operation (scalings, degassing). We thus recommend\\na new testing regime of at least 3 samples a year. While not a replacement for the detailed analyses, under the right circumstances, and when trained with more substantial data sets, viertual sensors provide a robust method in this setting to trigger further actions.\\n\",\"PeriodicalId\":7329,\"journal\":{\"name\":\"Advances in Geosciences\",\"volume\":\"27 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-05-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Advances in Geosciences\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5194/adgeo-58-189-2023\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"Earth and Planetary Sciences\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in Geosciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5194/adgeo-58-189-2023","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Earth and Planetary Sciences","Score":null,"Total":0}
Forecasting changes of the flow regime at deep geothermal wells based on high resolution sensor data and low resolution chemical analyses
Abstract. Geothermal waters provide a great resource to generate clean energy,
however, there is a notorious lack of high quality data on these
waters. The scarcity of deep geothermal aquifer information is
largely due to inaccessibility and high analysis costs.
However, multiple operators use geothermal wells in Lower Bavaria and Upper
Austria for balneological (medical and wellness) applications as well
as for heat mining purposes.
The state of the art sampling strategy budgets for a sampling frequency
of 1 year. Previous studies have shown that robust groundwater data
requires sampling intervals of 1–3 months, however, these
studies are based on shallow aquifers which are more likely to be
influenced by seasonal changes in meteorological conditions.
This study set out to assess whether yearly sampling adequately
represents sub-yearly hydrochemical fluctuations in the aquifer by
comparing yearly with quasi-continuous hydrochemical data
at two wells in southeast Germany by assessing mean, trend and
seasonality detection among the high and low temporal resolution
data sets. Furthermore, the ability to produce reliable forecasts
based on yearly data was examined. In order to test the applicability
of virtual sensors to elevate the information content of yearly data,
correlations between the individual parameters were assessed.
The results of this study show that seasonal hydrochemical
variations take place in deep aquifers, and are not adequately
represented by yearly data points, as they are typically gathered
at similar production states of the well and do not show varying
states throughout the year. Forecasting on the basis of
yearly data does not represent the data range of currently
measured continuous data. The limited data availability did not
allow for strong correlations to be determined.
We found that annual measurements, if taken at regular intervals and
roughly the same production rates, represent only a snapshot of the
possible hydrochemical compositions. Neither mean values, trends nor
seasonality was accurately captured by yearly data. This could lead to a violation of stability criteria for mineral water, or to problems in the geothermal operation (scalings, degassing). We thus recommend
a new testing regime of at least 3 samples a year. While not a replacement for the detailed analyses, under the right circumstances, and when trained with more substantial data sets, viertual sensors provide a robust method in this setting to trigger further actions.
Advances in GeosciencesEarth and Planetary Sciences-Earth and Planetary Sciences (miscellaneous)
CiteScore
3.70
自引率
0.00%
发文量
16
审稿时长
30 weeks
期刊介绍:
Advances in Geosciences (ADGEO) is an international, interdisciplinary journal for fast publication of collections of short, but self-contained communications in the Earth, planetary and solar system sciences, published in separate volumes online with the option of a publication on paper (print-on-demand). The collections may include papers presented at scientific meetings (proceedings) or articles on a well defined topic compiled by individual editors or organizations (special publications). The evaluation of the manuscript is organized by Guest-Editors, i.e. either by the conveners of a session of a conference or by the organizers of a meeting or workshop or by editors appointed otherwise, and their chosen referees.