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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 徐年盛(Nien-Sheng Hsu) | |
dc.contributor.author | Cherng-Tzong Chang | en |
dc.contributor.author | 張承宗 | zh_TW |
dc.date.accessioned | 2021-06-15T13:10:07Z | - |
dc.date.available | 2020-08-21 | |
dc.date.copyright | 2020-08-21 | |
dc.date.issued | 2020 | |
dc.date.submitted | 2020-08-11 | |
dc.identifier.citation | [1] 張智欽,2007,「開天闢地礁溪溫泉」,蘭陽博物館電子報,第25期。 [2] 吳秀珠,2012,「礁溪溫泉區地溫分布與數值模擬」,嘉南藥理科技大學溫泉產業研究所碩士論文。 [3] 賈欣鑫等,2014,「三維球、柱坐標系下導熱微分方程的離散求解」,重慶理工大學學報:自然科學版,28(1):P.33-37。 [4] 經濟部水利署,2019,「利用溫泉井水溫觀測資料推估地層導熱係數之研究」,嘉南藥理科技大學。 [5] Akhmetov, B., Georgiev, A., Popov, R., Turtayeva, Z., Kaltayev, A., Ding, Y., 2018. A novel hybrid approach for in-situ determining the thermal properties of subsurface layers around borehole heat exchanger. International J of Heat and Mass Transfer 126A, 1138-1149. [6] Bear, J., 1972. Dynamics of Fluids in Porous Media. New York: American Elsevier Publishing Company Inc. [7] Beck, A.E., Anglin, F.M., Sass, J.H., 1971. Analysis of heat flow data- in situ thermal conductivity measurements. Canadian J of Earth Sciences 8(1), 1-19. [8] Burkhardt, H., Honarmand, H., Pribnow, D., 1995. Test measurements with a new thermal conductivity borehole tool. Tectonophysics 244(1-3), 161-165. [9] Chang, C.T., Hsu, N.S., Chen, W.F., 2019. Using a 1D numerical temperature recovery model to estimate in-situ thermal diffusivity by an up-welling heat source in an artesian well. Geothermics 81, 143–151 [10] Chen, W.F., Chiang, H.T., 2016. Subsurface temperature trends in response to thermal water exploitation in the Jiashi Hot Spring, northeastern Taiwan. Geothermics 60: 126-133. [11] Chirila, M.A., Christoph, B., Vienken, T., Dietrich, P., Bumberger, J., 2016. Development of an in situ thermal conductivity measurement system for exploration of the shallow subsurface. Measurement Science and Technology 27(065901), 10pp. [12] deMarsily, G., 1986. Quantitative Hydrogeology. San Diego, California: Academic Press. [13] Deming, D., 2002. Introduction to Hydrogeology. New York:McGraw-Hill. [14] Domenico, P.A., and F.W. Schwartz., 1998. Physical and Chemical Hydrogeology, 2nd ed. New York: John Wiley Sons Inc. [15] Esen, H., Inalli, M., 2009. In-situ thermal response test for ground source heat pump system in Elazig, Turkey. Energy and Buildings 41(4), 395-401. [16] Freifeld, B.M., Finsterle, S., Onstott, T.C., Toole, P., Pratt, L.M., 2008. Ground surface temperature reconstructions: using in situ estimates for thermal conductivity acquired with a fiber-optic distributed thermal perturbation sensor. Geophysical Research Letters 35, L14309. [17] Goutorbe, B., Lucazeau, F., Bonneville, A., 2007. Comparison of several BHT correction methods: a case study on an Australian data set. Geophys. J. Int. 170(2), 913-922. [18] Gunzel, U., Wilhelm, H., 2000. Estimation of the in-situ thermal resistance of a borehole using the Distributed Temperature Sensing (DTS) technique and the Temperature Recovery Method (TRM). Geothermics, 29(6), 689-700. [19] Hamdhan, I.N., Clarke, B.G., 2010. Determination of thermal conductivity of coarse and fine sand soils. Proceedings World Geothermal Congress 2010, Bali, Indonesia, 25-29 April 2010. [20] Hausner, M. B., Suárez, F., Glander, K. E., Giesen, N. V. D., Selker, J. S., Tyler, S. W., 2011. Calibrating single-ended fiber-optic Raman spectra distributed temperature sensing data. Sensors, 11(11), 10859-10879. [21] Hermanrud, C., Cao, S., Lerche, I., 1990. Estimates of virgin rock temperature derived from BHT measurements: bias and error. Geophysics 55(7), 924-931. [22] Huenges, E., Will, G., 1989. Permeability, bulk modulus and complex resistivity in crystalline rocks. in Bridgwater, D., Fluid movements- element transport and the composition of the deep crust, NATO ASI, C281 (Kluwer, Dordrecht), 361-375. [23] Huenges, E., Ledru, P., 2011. Geothermal energy systems: exploration, development, and utilization. John Wiley Sons, 486 pages. [24] Ingebritsen, S.E., and W.E. Sanford., 1998. Groundwater in Geologic Processes. Cambridge, UK: Cambridge University Press. [25] Jaeger, J. C., 1956. Numerical values for the temperature in radial heat flow, J. Math. Phys. 34, 316. [26] Jeager, J. C., 1961. The effect of the drilling fluid on temperatures measured in boreholes. J. Geophys. Res. 66, 563-569. [27] Kennedy, J. and Eberhart, R.C., 1995. Particle Swarm Optimization. Proc. IEEE International Conf. Neural Networks, 4, 1942-1948, Perth, WA, Australia, 27 Nov.-1 Dec.. [28] Kutasov, I.M., Eppelbaum, L.V., 2005. Determination of formation temperature from bottom-hole temperature logs-a generalized Horner method. J. Geophys. Eng. 2(2), 90-96. [29] Lee, T.C., 1982. Estimation of formation temperature and thermal property from dissipation of heat generated by drilling. Geophysics 47(11), 1577-1584. [30] Lee, T.C., Duchkow, A.D., Morozov, S.G., 2003. Determination of thermal properties and formation temperature from borehole thermal recovery data. Geophysics 68(6) , 1835-1846. [31] Luheshi, M.N., 1983. Estimation of formation temperature from borehole measurements. Geophys. J. R. astr. Soc. 74(3), 747-776. [32] Middleton, M. F., 1979. A model for bottom hole temperature stabilization. Geophysics 44(8), 1458-1462. [33] Musmann, G., Kessels, W., 1980. An In-Situ Thermal Conductivity Probe. In: Strub, A.S., Ungemach, P. (eds) Advances in European Geothermal Research. Springer, Dordrecht. [34] Palaitis, Z., Indriulionis, A., 2012. Evaluation of ground thermal properties and specification of the geological structure using thermal response test, natural gamma, and resistivity data. Geologija 54(4), 125-135. [35] Raymond, J., Therrien, R., Gosselin, L., 2011. Borehole temperature evolution during thermal response tests. Geothermics 40(1), 69-78. [36] Raymond, J., Lamarche, L., Malo, M., 2016. Extending thermal response test assessments with inverse numerical modeling of temperature profiles measured in ground heat exchangers. Renewable Energy 99, 614–621. [37] Raymond, J., 2018. Colloquium 2016: assessment of the subsurface thermal conductivity for geothermal applications. Canadian Geotechnical J 55(9), 1209-1229. [38] Sanner, B., Hellström, G., Spitler; J. and Gehlin, S., 2005. Thermal Response Test – current status and world-wide application. Proceedings World Geothermal Congress 2005 Antalya, Turkey, 24-29 April. [39] Santoyo, E., Garcia, A., Espinosa, G., Hernandez, I., Santoyo, S., 2000. STATIC_TEMP: a useful computer code for calculating static formation temperatures in geothermal wells. Computers Geosciences 26, 201-217. [40] Sauer, D., Wagner, S., Amro, M., Popov, Y., Rose, F., Schramm, A., Borner, E., Wunsch, T., Redondo-Robles, H., Hesse, G., Pfeiffer, J., 2017. Development of a new borehole probe for thermal conductivity scanning. Geothermics 67, 95-101. [41] Seibertz, K.S.O., Chirila, M.A., Bumberger, J., Vienken, T., 2016. Development of in-aquifer heat testing for high resolution subsurface thermal-storage capability characterization. J. of Hydrology 534, 113-123. [42] Wilhelm, H., 1990. A new approach to the borehole temperature relaxation method. Geophys. J. Int. 103, 469-481. [43] Witte, H.J.L., van Gelder, G.J., Spitler, J.D., 2002. In situ measurement of ground thermal conductivity: the Dutch perspective. ASHRAE Transactions 108(1), 263-272. [44] Zhao, D., Qian, X., Gu, X., Jajja, S.A., Yang, R., 2016. Measurement techniques for thermal conductivity and interfacial thermal conductance of bulk and thin film materials. J. of Electronic Packaging. 138(4). | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/50975 | - |
dc.description.abstract | 受壓含水層之溫泉井如同一般地下水井,隨著季節性的降雨補注會造成水壓上升,當井內水壓高過地表高程時,會有自湧現象的發生。井下高溫深層水因自湧上升時,會造成井體增溫,隨著溫泉自湧的持續,井中水溫會趨於穩定,當自湧現象結束時,井中水溫會因為地層熱傳導效應,逐漸下降至自湧前的初始溫度為止,此期間稱為熱恢復期(Temperature Recovery:TR)。熱恢復期溫泉觀測井之溫度時間序列,可利用地層熱傳導數學模型加以模擬,而地層熱傳導係數也可以透過熱恢復期溫泉觀測井現地實測之水溫數據,進行率定的工作。 溫泉井自湧現象受限於降雨及含水層地質等條件的影響,不確定性甚高,其發生之時間與位置無法事先預知,因此,即時且連續之水溫及水位資料觀測難以完整獲得。本研究提出除了溫泉自湧的觀測以外,可藉由抽水試驗方式,使溫泉井下高溫深層水上升,並於井中架設光纖量測系統進行溫度量測,當停止抽水後,水溫下降進入熱恢復期,利用井中的不同深度水溫時間序列變化數據,再透過熱傳導數值模擬以及參數優選模式,可進行各深度地層熱傳導係數之率定,以及相關地層岩性之判識。 由於溫泉井的深度少則幾十公尺,多則數百甚至超過上千公尺,井深遠大於井管的尺寸,當溫泉井水位及流速變動微小時,則井體熱損失主要由井體通過井壁,以熱傳導的方式傳向地層,所以可以將其近似為徑向一維圓柱熱傳導模型。熱傳導數值模擬模式之參數優選,則採用粒子群聚演算法(Particle Swarm Optimization, PSO),該法係源於人類觀察鳥類覓食行為所發展出來的最佳化演算法工具,粒子群聚演算法具有簡單易行、收斂速度快、設置參數少等優點,已廣泛的被採用於多變量系統分析參數優選的議題研究上。 本研究蒐集國外兩組文獻中的數據:德國黑森林及北海鑽井,來說明所提出地層熱傳導模式與熱傳參數率定的方法,其結果與文獻現地試驗分析結果相當吻合;並進一步在2019年7月23-25日於礁溪溫泉區奇利丹溫泉井觀測井進行現地抽水試驗來產生井內熱源,並量測井中水溫之時間序列變化。現地試驗以空壓機打氣方式(Air-lift),使井管內水體存在密度差異造成流動,井底高水壓將水體補充至上部,藉此讓井管內部溫度上升。當整口井管內的水溫達到穩定後,停止抽水,井體內部水位與溫度將逐漸回復至初始背景值,光纖纜線將全程於井下不同深度進行溫度量測,取樣時間設定為10分鐘一筆,溫度解析度則為±0.1℃。透過本研究試驗,在沒有自湧的時期,以抽水來使深層熱水往上流,然後利用此溫度差可推求各地層分層之熱傳導係數,而抽水試驗熱恢復期溫度量測時間的調整,則應該考量避免受到溫泉業者抽水影響的干擾。 研究結果顯示:礁溪溫泉奇利丹井不同深度之熱傳導係數,推測分成三層:深度0-50 m熱傳導係數2.57-2.89 W/m℃,推測岩性為砂層;深度55-65 m熱傳導係數1.11-1.68W/m℃,推測岩性為泥層;深度70-100 m熱傳導係數4.04-5.72 W/m℃,推測岩性為礫石層,所得出的數值與當地工程經驗相符。本研究在礁溪溫泉進行現地熱傳導係數研究,此方法經確立後將也可應用於其他溫泉區。 | zh_TW |
dc.description.abstract | As with typical groundwater wells, hot spring wells drilled into a confined aquifer experience increases in water pressure following seasonal precipitation, and consequently, upwelling occurs. The well temperature then increases as high-temperature deep water rises; as upwelling continues, the water temperature within the well becomes stable. When upwelling ends, the well temperature gradually decreases to the prior level due to thermal conduction in strata; this process is known as temperature recovery (TR). The temperature time series of hot-spring observation wells during TR can be simulated using a mathematical model of stratum thermal conduction; the thermal conductivity (TC) coefficients in strata can be calibrated according to water temperatures that are actually measured at observation wells during TR. The time and location of upwelling at hot spring wells are highly uncertain because the phenomenon is affected by conditions including precipitation and aquifer geology. To address this problem, this study proposed an alternative of hot spring upwelling observation. Using a pumping test, the proposed method allowed the high-temperature deep water at the well bottom to rise. An optical fiber measuring system was installed in the well to measure water temperatures. After the pumping was stopped, water temperature decreased and entered the TR stage. Researchers could then obtain time series data of water temperatures at different depths, simulate TC values, and construct a parameter optimization model to calibrate the TC coefficient at each stratum and determine relevant stratum properties. The depth of a hot spring well can span from dozens of meters to hundreds or even thousands of meters. When changes in water level and flow rate within a well are small, heat energy passes through the well wall and transfers to other strata through thermal conduction, causing heat loss. A unidimensional cylinder model can be used to simulate the thermal conduction process. For the parameter optimization of the simulation model, particle swarm optimization (PSO) was adopted. This algorithm was inspired by foraging birds. PSO is simple with a fast convergence rate, and it requires few parameters. The algorithm has been widely applied to research on parameter optimization in multivariate system analysis. Data from two nondomestic studies concerning the Black Forest in Germany and North Sea well drilling were extracted to verify the proposed stratum thermal conduction model and TC parameter calibration method. The verification results were consistent with the on-site results in the studies. The authors also performed an on-site pumping test between July 23 and July 25, 2019 in an observation well of Cilidan, a hot spring located in Jiaoxi in Yilan County, Taiwan. The test was conducted to generate heat in the well and measure temporal variations in water temperature. The experiment was performed using an air-lift compressor to create differences in density in water in the well casing and cause fluid flow. The high water pressure at the well bottom forced the water to fill up the well, and the temperature of the well casing increased. Pumping was stopped after the water in the well casing reached a stable temperature, after which the water level and temperature in the well resumed their initial background values. During the process, optical fibers were employed to measure temperatures at various depths; the sampling frequency was set to every 10 min, with the temperature resolution being ±0.1°C. In the experiment, when upwelling was absent, the researchers could pump water up so that deep water rose; the resulting temperature differences enabled the computation of TC coefficients at various strata. Notably, temperature measurements during the TR of the pumping test should be adjusted to avoid interference caused by water pumping of hot spring resorts. The experimental results are as follows. In the Cilidan hot spring well, the TC coefficients could be divided into three levels as follows: (1) At a depth of 0–50 m, the TC coefficients were 2.57–2.89 W/m°C; accordingly, the lithology of this stratum mainly comprised sand. (2) At a depth of 55–65 m, the TC coefficients ranged between 1.11 and 1.68 W/m°C; hence, this stratum was speculated to be a mud layer. (3) At a depth of 70–100 m, the TC coefficients were 4.04–5.72 W/m°C, and the lithology indicated the presence of gravel. These values correspond with expectations from previous findings on on-site constructions. This study confirmed that the on-site TC coefficient experiment conducted in Jiaoxi can be performed in other hot spring areas. | en |
dc.description.provenance | Made available in DSpace on 2021-06-15T13:10:07Z (GMT). No. of bitstreams: 1 U0001-1008202016562100.pdf: 9721533 bytes, checksum: e08f41c3104be6ee5e19fb830a808921 (MD5) Previous issue date: 2020 | en |
dc.description.tableofcontents | 口試委員會審定書 i 誌謝 ii 中文摘要 iii 英文摘要 v 目錄 viii 圖目錄 x 表目錄 xii 第一章 前言 1 1.1 研究動機 1 1.2 文獻回顧 2 1.3 研究目的 5 1.4 研究內容 5 1.5 論文章節 6 第二章 溫泉井熱傳導數值模式 7 2.1 熱傳導控制方程式 7 2.1.1 三維熱傳導控制方程 7 2.1.2 一維熱傳導控制方程 7 2.2 熱傳導數值模式 8 2.2.1 有限容積法 9 2.2.2 熱傳導離散方程 10 2.2.3 一維徑向熱傳導離散方程 12 2.2.4 穩定性與收斂性 14 第三章 地層熱傳導參數率定優選模式 15 3.1 熱傳導參數優選模式 16 3.2 優選模式求解 16 3.2.1 粒子群聚演算法 16 3.2.2 演算法流程 18 3.2.3 程式的撰寫 19 第四章 光纖溫度量測系統 21 4.1 分散式光纖溫度感測系統之原理 21 4.2 分散式光纖溫度感測器基本元件 23 4.3 現地井下量測配置 25 4.4 分散式光纖溫度感測器之量測能力 26 第五章 實例應用研究 27 5.1 文獻案例 27 5.1.1 德國黑森林探井 27 5.1.2 北海探井 29 5.2 礁溪溫泉區案例 30 5.2.1 區域概述與奇利丹井特性 30 5.2.2 井溫觀測與分析 35 5-2-3 熱傳參數優選與模擬誤差 43 5-2-4 地層岩性推估 51 第六章 結論與建議 52 6.1 結論 52 6.2 建議 53 參考文獻 55 附錄 原始程式碼 58 | |
dc.language.iso | zh-TW | |
dc.title | 以監測井之溫度時間序列推算溫泉區地層之現地熱傳導係數 | zh_TW |
dc.title | Using temperature time series data in a monitoring well to estimate the in-situ thermal conductivity of a hot spring formation | en |
dc.type | Thesis | |
dc.date.schoolyear | 108-2 | |
dc.description.degree | 博士 | |
dc.contributor.oralexamcommittee | 李振誥(Cheng-Haw Lee),劉振宇(Chen-Wuing Liu),陳文福(Wen-fu Chen),邱永嘉(Yung-Chia Chiu) | |
dc.subject.keyword | 溫泉觀測井,熱傳導係數,粒子群聚演算法,自湧現象,熱恢復,熱傳導數學模型,抽水試驗, | zh_TW |
dc.subject.keyword | hot spring observation well,thermal conductivity coefficient,particle swarm optimization,upwelling,thermal recovery,mathematical model of thermal conduction,pumping test, | en |
dc.relation.page | 62 | |
dc.identifier.doi | 10.6342/NTU202002841 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2020-08-12 | |
dc.contributor.author-college | 工學院 | zh_TW |
dc.contributor.author-dept | 土木工程學研究所 | zh_TW |
顯示於系所單位: | 土木工程學系 |
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