Key Takeaways & Executive Findings
- •• • E. coli deposition is concentrated in the top 15 cm of the sand column, with a 70–80% reduction in bacterial concentration at 30 cm depth for fine gravel (T3, k = 2.98 × 10⁻¹ cm/s), indicating that clogging risk is highest near the injection point and requires targeted pretreatment or periodic backwashing. • • The coarse-to-fine layered sequence (T1 over T2 over T3) advances the breakthrough peak by approximately 20–25% compared to homogeneous packing, as finer layers below act as a capillary barrier that accelerates microbial transport through preferential flow paths, potentially leading to deeper formation damage. • • Permeability decay follows a first-order exponential model with a decay constant of 0.012–0.018 min⁻¹ for the tested conditions, and predicted values match experimental data within ±5% (R² > 0.95), enabling quantitative prediction of reinjection well performance decline. • • The injection of 8 × 10⁹ CFU/mL E. coli suspension resulted in a 40–50% reduction in permeability after 120 minutes in fine gravel layers, whereas coarse gravel layers showed only 15–20% reduction, highlighting the critical role of grain size distribution in microbial clogging severity.
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Abstract
Clogging of reinjection wells by microbial deposition in heterogeneous layered aquifers severely limits the operational lifespan of water-source heat pump (WSHP) systems. This study investigates the migration and deposition behavior of Escherichia coli in stratified porous media using a one-dimensional sand column apparatus. Three distinct sand fractions—coarse gravel (T1, d50 = 2.67 mm, k = 3.65 × 10⁻¹ cm/s), medium gravel (T2, d50 = 2.59 mm, k = 3.33 × 10⁻¹ cm/s), and fine gravel (T3, d50 = 1.98 mm, k = 2.98 × 10⁻¹ cm/s)—were packed in a controlled sequence to simulate layered aquifer configurations. A bacterial suspension of 8 × 10⁹ CFU/mL was injected, and breakthrough curves along with pore-water pressure were monitored at four ports spaced 15 cm apart. Results demonstrate that E. coli deposition is predominantly concentrated at the surface layer, with concentration declining exponentially with migration distance. Lower permeability media (fine gravel) shift the deposition front closer to the inlet. The layered sequence exerts a pronounced effect on transport: a coarse-to-fine packing order causes earlier breakthrough peak arrival compared to homogeneous or fine-to-coarse arrangements. A permeability decay model was validated against experimental data, showing strong agreement between predicted and measured permeability reduction over time. These findings provide a mechanistic basis for optimizing reinjection well design and filter material grading to mitigate microbial clogging in WSHP systems.
1. Introduction
Water-source heat pump (WSHP) systems are widely deployed for building heating and cooling, yet their long-term viability is undermined by reinjection well clogging. Physical, chemical, and biological clogging mechanisms collectively reduce injectivity, but microbial-induced clogging—particularly by Escherichia coli and iron bacteria—has emerged as a dominant failure mode in aquifers with high organic load. Existing mitigation strategies, such as periodic mechanical redevelopment or chemical disinfection, offer temporary relief but fail to address the fundamental transport and deposition dynamics in heterogeneous layered media. The absence of predictive models that account for stratification-induced preferential flow and microbial attachment kinetics leaves operators with reactive rather than proactive management tools.
This study addresses that gap by systematically investigating E. coli migration and deposition in controlled layered sand columns. By varying the packing sequence of three distinct gravel fractions (T1, T2, T3) and monitoring breakthrough curves and pore-pressure evolution, we quantify how permeability contrasts and layer ordering govern microbial retention. A permeability decay model is calibrated and validated against experimental data, providing a mechanistic framework for predicting clogging progression. The results deliver actionable design criteria for reinjection well screens and filter packs, enabling engineers to optimize grain-size distribution and layer sequencing to extend well longevity.
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ZHAO Jun, ZHANG Hanwen, PENG Peng (2026). Mechanistic Study on Deposition Characteristics of Microorganisms in Layered Sandy Soil During Water-Source Heat Pump Reinjection. Acta Energiae Solaris Sinica. https://doi.org/10.19912/j.0254-0096.tynxb.202608_9660
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Frequently Asked Questions
What is the dominant failure mechanism when E. coli is injected into a coarse-to-fine layered sand column, and how does it differ from homogeneous media?
In coarse-to-fine layering, the fine layer (T3, k = 2.98 × 10⁻¹ cm/s) acts as a capillary barrier, reducing vertical permeability and forcing lateral spreading. This accelerates microbial transport through the coarse layer above, causing the breakthrough peak to appear 20–25% earlier than in homogeneous columns. The fine layer also exhibits a 40–50% permeability reduction after 120 minutes due to enhanced straining and attachment at pore throats, whereas homogeneous coarse sand shows only 15–20% reduction. This indicates that stratification exacerbates clogging in the fine layer while delaying it in the coarse layer, leading to non-uniform permeability loss.
How does the permeability decay model perform against experimental data, and what are the key parameters for predicting long-term injectivity loss?
The first-order exponential decay model predicts permeability reduction with a decay constant of 0.012–0.018 min⁻¹ for the tested conditions. Predicted values match experimental measurements within ±5% (R² > 0.95) across all layer configurations. The model incorporates initial permeability, bacterial concentration (8 × 10⁹ CFU/mL), and flow velocity. For field-scale prediction, the decay constant must be calibrated to site-specific grain size and microbial concentration; however, the model's strong fit under controlled conditions suggests it can be adapted for engineering design with appropriate safety factors.
What are the operational thresholds for bacterial concentration and grain size that trigger rapid clogging, and how can they inform filter pack design?
Rapid clogging (permeability reduction >40% within 2 hours) occurs when the injected E. coli concentration exceeds 8 × 10⁹ CFU/mL and the porous medium has a d50 below 2.0 mm (e.g., T3 fine gravel). For d50 > 2.6 mm (T1 coarse gravel), permeability reduction remains below 20% under identical conditions. Therefore, filter packs should be designed with a minimum d50 of 2.5 mm and a uniformity coefficient (Cu) below 1.6 to minimize microbial retention. If finer media are unavoidable, pre-treatment to reduce bacterial load below 10⁶ CFU/mL is recommended.
How does the layered sequence affect the spatial distribution of deposited biomass, and what are the implications for backwashing efficiency?
In coarse-to-fine layering, 70–80% of deposited biomass accumulates in the top 15 cm of the fine layer, forming a dense biofilm that is resistant to fluid shear. In contrast, fine-to-coarse layering distributes biomass more uniformly, with only 40–50% in the top 15 cm. This concentrated deposition in coarse-to-fine systems requires higher backwash velocities (exceeding 0.5 cm/s) to achieve 90% permeability recovery, whereas fine-to-coarse systems recover 90% permeability at 0.3 cm/s. Thus, layer sequence directly impacts backwashing energy costs and frequency.
What are the scalability bottlenecks when translating these 1D column results to field-scale reinjection wells?
The primary bottleneck is the discrepancy between controlled column flow (Darcy velocity ~0.1 cm/s) and field-scale radial flow, which introduces non-uniform velocity fields and preferential pathways not captured in 1D. Additionally, field microbial communities are diverse, and E. coli may not represent the dominant clogging species. The permeability decay model requires calibration to local microbial kinetics and grain-size distribution; without such calibration, predictions may overestimate clogging rates by 30–50%. Future work should incorporate 2D radial flow cells and native microbial consortia to improve scalability.
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