成果專區 - 土壤及地下水污染整治基金補助研究與模場專案宣導網

專案基本資料

年度	110	專案性質	實驗性質
專案類別	模場試驗	研究主題	調查
申請機構	國立交通大學	申請系所	土木工程學系
專案主持人	張良正	職等/職稱	教授
專案中文名稱	新型光纖光柵高解析監測技術於深層水文地質參數異質場調查技術之發展
中文關鍵字	水文地質參數,光纖光柵感測器,非均質性,聯合反演,深層含水層
專案英文名稱	Development of a novel technology to identify the heterogeneity of deep aquifer using distributed fiber Bragg grating sensors
英文關鍵字	Hydrogeological parameters, fiber Bragg grating sensor, heterogeneity, joint inversion, deep aquifer
執行金額	2,290,000元
執行期間	2021/1/1 至 2021/12/31
計畫中文摘要	為了預測藥劑與污染團在深層含水層的移動路徑，求得高解析度的三維水文地質參數場是相當重要的，然而傳統的抽水試驗與微水試驗因為其理論假設與現地特性不符，因此無法求得真實參數異質場。因此，建立一個新技術來調查深層含水層的水文地質參數場實為刻不容緩。本研究將以本團隊於前期土基會計畫「新型光纖光柵多深度監測於水文地質與熱傳參數異質場推估技術之發展」之研究成果為基礎，發展新的深層三維參數場調查技術，此技術包含三個部分: (1)光纖光柵(FBG)系統改善: 此部分包含修改橡皮膜與感測器以適應深層含水層的水壓、加入水質感測器以直接觀測整治藥劑的移動情況，及加入注藥管以在調查階段即可利用觀測井進行水力掃描。(2)奇異值分解: 此方法將用於水力掃描演算法中，以降低現地案例分析時之大量計算量需求。(3)分布式光纖感測器(DTS) ：本計畫第二年時，此設備將用來量測高解析度的井內溫度剖面，這些觀測將用於改善水力掃描的推估結果。本研究延續第一年的研究基礎，已在本年度完成兩次現地試驗，第一次試驗（三月）主要比較新開發之光柵水溫水壓計與電子式水溫水壓計之量測精度與反應速度，結果顯示兩類感測器在精度與反應速度相當接近，顯現光柵水溫水壓計之準確性與實用性。第二次測試（四月）則進行跨孔多深度水溫與水壓量測，結果顯示注水產生之水壓變化皆可在兩口光柵多深度觀測井觀察到，但水溫在跨孔感測器上則無明顯變化，因為地下水流速慢，灌注水的水溫無法在灌注時間內傳遞到鄰井。第三次試驗（九月）進行新型態模組試驗，然因為鋼索過細導致鋼索斷裂，無法完成測試。本年度11月於現場測試新型態模組試驗，然可能因為井管彎曲導致設備僅能置入20公尺深，無法完成測試。本年度十二月替換場址，並順利完成兩口井之設備安裝，安裝深度達地下五十五公尺，並搭配瑞昶公司注藥順利完成水力掃描。同時現場亦進行地電阻剖面掃描。水力掃描成果顯示，本研究使用注藥期間蒐集之多組觀測資料推估場址之三維水文地質參數場，推估結果再與跨孔井下電阻施測所得電阻率變化比較，兩者結果一致，顯示推估參數之合理性。最後，本研究以推估之參數場設計案例，模擬藥劑灌注後之三維傳輸路徑與流佈，以找出藥劑之主要流向與無法抵達之區域（盲點區域），說明此模式具有展現藥劑三維流布之能力，可有效協助廠商辨識藥劑傳輸路徑並適當調整注藥策略，以期讓藥劑可抵達注藥盲點區域，充分與污染物混合。後續本研究將持續與整治廠商進一步合作，將其注藥規劃輸入模式中模擬，並提供廠商注藥後之可能三維藥劑流佈資訊，以提升其污染整治之成效。
計畫英文摘要	To access and predict the movement of the plume and agent in the deep aquifers, understanding the detailed hydrogeological parameters fields is essential. However, using the traditional pumping and slug tests to develop these three-dimensional parameter fields is impossible due to their unrealistic assumptions. Accordingly, developing new technology to identify the three-dimensional hydrogeological parameter fields is urgent, and we will create a new method based on the project results. "Development of a novel technology to identify the heterogeneous hydrogeological and geothermal parameters by using multi-depths fiber Bragg grating sensors" funded by Taiwanese EPA. The newly developed technology consists of three parts: (1) Improvement of the fiber Bragg grating sensing (FBG) system, including adjusting the rubber packer, piezometer, and thermometer to adapt to the high groundwater pressure. Electronic water quality sensors will also be installed into the FBG system to observe the movement of the remediation agent. To conduct hydraulic tomography (HT) using groundwater monitoring wells in the early stage of site remediation, we install several injection tubes in the new FBG system. (2) Singular value decomposition: This method will be used to reduce HT's computational loading for the field case study. (3) Distributed temperature sensor (DTS): In the second year, we will use DTS to measure the temperature in the wells with high spatial resolution. The measurements will be used to improve the estimate of hydrogeological parameters from HT using head data. This study has conducted two field tests in March and April. The first test compares the accuracy and response time of FBG sensors with the electronic-based sensors, and the results show that their accuracy and response time are almost identical. The second test is to examine the capability of the FBG monitoring system on depth-discrete measurements. The results indicate that the FBG system can measure the head variations at the various given depths of selected wells. Also, the measured temperature remains stable at the well away from the injection well. This is because the slow groundwater velocity cannot transport heat from the injection well to the nearby well during the injection period. Besides the FBG experiments, we have conducted an electrical resistivity survey to understand the sediment structure of the study site and valid the estimated K fields. The results show that the obtained resistivity image profiles are consistent with the estimated K fields. Finally, the estimated K and Ss fields simulate the remediation agent transport. Once the spatial distribution of the remediation agent is obtained, we can know the area where the agent cannot reach and modify the remediation agent injection strategy to make the agent contact the plume. Finally, we will continue to cooperate with remediation manufacturers to provide them with the spatial distribution of agents and help them to improve their agent injection strategy.

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