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M. Brehme
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This study addresses the critical aspects of injection well clogging as part of an Aquifer Storage, Transfer and Recovery (ASTR) pilot infiltrating tile drainage water (TDW) from agricultural field into an anoxic brackish sandy aquifer for later re-use as irrigation water for flower bulbs. Two ASTR injection periods suffering from clogging were intensely monitored during several weeks in 2019 to 2020. The wells were rehabilitated by methods of backflushing and mechanical cleaning (high-pressure jetting) and sampled to obtain information on the kind of clogging material. The recharge water is nutrient rich (PO4: 2-18 mgL-1, NO3: 6-50 mgL-1, NH4: 0.1-0.6 mgL-1, DOC: 25-32 mgL-1) and attained significantly turbid conditions (usually between 5-20 NTU; up to 160 NTU) caused by the removal of resuspended biochemical material in the tile drainage network by extreme precipitation and extensive drain discharge events. Consequentially, the 40 µm spin Klin-disc filters as pre-treatment step experienced reduced functionality due to clogging when subjected to high turbid loads. Microbial processes within the ASTR piping system additionally caused fluctuations in turbidity by periodic on/off operation, indicating a high potential for (bio)physical and biological clogging in the injection wells. Microscopic and (hydro)geochemical analysis of injectant and backflushed suspended solids demonstrated a significant contribution of injected Fe-hydroxyapatite flocs and biochemical material incorporating siliceous clay and silt particles below 40 µm, while pyrite and possibly calcite precipitants were flushed from the aquifer matrix when removing suspended matter from the wells. The injection of suspended material caused clogging predominantly by physical mechanisms. During standstill, the hydrochemical environment in the well indicated substantial microbial activity by reduced redox conditions in the well and the mobilization of Fe(II), Mn(II), P, and Ca, indicating the vulnerability for biological clogging. Improving the feasibility of the ASTR system requires the high clogging potential of TDW to be reduced. It is recommended to adopt a settling tank, rapid sand filtration followed by slow sand filtration as pre-treatment steps to reduce the physical and biological clogging potential. Various other recommendations to reduce the clogging risks are discussed further. Regarding the well rehabilitation, methods of compressed-air ‘jutteren’ are recommended to recovery the well performance by effectively removing residual clogging material from the gravelpack and borehole wall.
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This study addresses the critical aspects of injection well clogging as part of an Aquifer Storage, Transfer and Recovery (ASTR) pilot infiltrating tile drainage water (TDW) from agricultural field into an anoxic brackish sandy aquifer for later re-use as irrigation water for flower bulbs. Two ASTR injection periods suffering from clogging were intensely monitored during several weeks in 2019 to 2020. The wells were rehabilitated by methods of backflushing and mechanical cleaning (high-pressure jetting) and sampled to obtain information on the kind of clogging material. The recharge water is nutrient rich (PO4: 2-18 mgL-1, NO3: 6-50 mgL-1, NH4: 0.1-0.6 mgL-1, DOC: 25-32 mgL-1) and attained significantly turbid conditions (usually between 5-20 NTU; up to 160 NTU) caused by the removal of resuspended biochemical material in the tile drainage network by extreme precipitation and extensive drain discharge events. Consequentially, the 40 µm spin Klin-disc filters as pre-treatment step experienced reduced functionality due to clogging when subjected to high turbid loads. Microbial processes within the ASTR piping system additionally caused fluctuations in turbidity by periodic on/off operation, indicating a high potential for (bio)physical and biological clogging in the injection wells. Microscopic and (hydro)geochemical analysis of injectant and backflushed suspended solids demonstrated a significant contribution of injected Fe-hydroxyapatite flocs and biochemical material incorporating siliceous clay and silt particles below 40 µm, while pyrite and possibly calcite precipitants were flushed from the aquifer matrix when removing suspended matter from the wells. The injection of suspended material caused clogging predominantly by physical mechanisms. During standstill, the hydrochemical environment in the well indicated substantial microbial activity by reduced redox conditions in the well and the mobilization of Fe(II), Mn(II), P, and Ca, indicating the vulnerability for biological clogging. Improving the feasibility of the ASTR system requires the high clogging potential of TDW to be reduced. It is recommended to adopt a settling tank, rapid sand filtration followed by slow sand filtration as pre-treatment steps to reduce the physical and biological clogging potential. Various other recommendations to reduce the clogging risks are discussed further. Regarding the well rehabilitation, methods of compressed-air ‘jutteren’ are recommended to recovery the well performance by effectively removing residual clogging material from the gravelpack and borehole wall.
The hot water produced from a geothermal doublet possesses energy, which once utilized, the water cools down and is re-introduced back into the same reservoir at a sufficient distance using an injector well. As cold water flows through the reservoir, it acquires thermal energy from surrounding in-situ rocks. This process recurs until a substantial drop in rock temperature occurs and the water is unable to be recharged adequately; as a result, cold water starts to "break-through" into the production well and the doublet soon needs to be abandoned.
This breakthrough time can be predicted using reservoir simulation. Significant work has been done in the past to determine the effect of different parameters on the breakthrough time and accuracy of models have been improved by incorporating real world physics. Despite being capable to predict breakthrough time, accurate high-fidelity 3D models require significant time in uncertainty quantification and data assimilation analysis due to CPU demanding simulations.
In this project, a physics-based proxy model is developed to predict flow and heat transport in low-enthalpy reservoirs. Streamlines that describe flow in a system and mostly controlled by steady-state pressure distribution are traced using Pollock's method (Pollock, 1988) and the reservoir is divided into streamtubes. Rock-heat depletion is modelled by semi-analytic model along streamlines. The objective is to predict geothermal doublet breakthrough time using only a limited number of streamtubes, thus minimizing simulation time and CPU resources.
Comparison with accurate high-fidelity model reveals that results for proxy model are optimistic; the error for pressure and temperature distributions, as well as the breakthrough curves is within the acceptable tolerance. The time required to simulate the proxy-model is less than the high-fidelity model. And as the number of streamtubes (to simulate the proxy model with) decrease, the time required for simulation further decreases but conversely the error between the breakthrough curves increases.
...
This breakthrough time can be predicted using reservoir simulation. Significant work has been done in the past to determine the effect of different parameters on the breakthrough time and accuracy of models have been improved by incorporating real world physics. Despite being capable to predict breakthrough time, accurate high-fidelity 3D models require significant time in uncertainty quantification and data assimilation analysis due to CPU demanding simulations.
In this project, a physics-based proxy model is developed to predict flow and heat transport in low-enthalpy reservoirs. Streamlines that describe flow in a system and mostly controlled by steady-state pressure distribution are traced using Pollock's method (Pollock, 1988) and the reservoir is divided into streamtubes. Rock-heat depletion is modelled by semi-analytic model along streamlines. The objective is to predict geothermal doublet breakthrough time using only a limited number of streamtubes, thus minimizing simulation time and CPU resources.
Comparison with accurate high-fidelity model reveals that results for proxy model are optimistic; the error for pressure and temperature distributions, as well as the breakthrough curves is within the acceptable tolerance. The time required to simulate the proxy-model is less than the high-fidelity model. And as the number of streamtubes (to simulate the proxy model with) decrease, the time required for simulation further decreases but conversely the error between the breakthrough curves increases.
...
The hot water produced from a geothermal doublet possesses energy, which once utilized, the water cools down and is re-introduced back into the same reservoir at a sufficient distance using an injector well. As cold water flows through the reservoir, it acquires thermal energy from surrounding in-situ rocks. This process recurs until a substantial drop in rock temperature occurs and the water is unable to be recharged adequately; as a result, cold water starts to "break-through" into the production well and the doublet soon needs to be abandoned.
This breakthrough time can be predicted using reservoir simulation. Significant work has been done in the past to determine the effect of different parameters on the breakthrough time and accuracy of models have been improved by incorporating real world physics. Despite being capable to predict breakthrough time, accurate high-fidelity 3D models require significant time in uncertainty quantification and data assimilation analysis due to CPU demanding simulations.
In this project, a physics-based proxy model is developed to predict flow and heat transport in low-enthalpy reservoirs. Streamlines that describe flow in a system and mostly controlled by steady-state pressure distribution are traced using Pollock's method (Pollock, 1988) and the reservoir is divided into streamtubes. Rock-heat depletion is modelled by semi-analytic model along streamlines. The objective is to predict geothermal doublet breakthrough time using only a limited number of streamtubes, thus minimizing simulation time and CPU resources.
Comparison with accurate high-fidelity model reveals that results for proxy model are optimistic; the error for pressure and temperature distributions, as well as the breakthrough curves is within the acceptable tolerance. The time required to simulate the proxy-model is less than the high-fidelity model. And as the number of streamtubes (to simulate the proxy model with) decrease, the time required for simulation further decreases but conversely the error between the breakthrough curves increases.
This breakthrough time can be predicted using reservoir simulation. Significant work has been done in the past to determine the effect of different parameters on the breakthrough time and accuracy of models have been improved by incorporating real world physics. Despite being capable to predict breakthrough time, accurate high-fidelity 3D models require significant time in uncertainty quantification and data assimilation analysis due to CPU demanding simulations.
In this project, a physics-based proxy model is developed to predict flow and heat transport in low-enthalpy reservoirs. Streamlines that describe flow in a system and mostly controlled by steady-state pressure distribution are traced using Pollock's method (Pollock, 1988) and the reservoir is divided into streamtubes. Rock-heat depletion is modelled by semi-analytic model along streamlines. The objective is to predict geothermal doublet breakthrough time using only a limited number of streamtubes, thus minimizing simulation time and CPU resources.
Comparison with accurate high-fidelity model reveals that results for proxy model are optimistic; the error for pressure and temperature distributions, as well as the breakthrough curves is within the acceptable tolerance. The time required to simulate the proxy-model is less than the high-fidelity model. And as the number of streamtubes (to simulate the proxy model with) decrease, the time required for simulation further decreases but conversely the error between the breakthrough curves increases.