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Research progress on temperature field evolution of hot reservoirs under low-temperature tailwater reinjection
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Abstract: This paper focuses on the study of the evolutionary mechanism governing the temperature field of geothermal reservoir under low-temperature tailwater reinjection conditions, which is crucial for the sustainable geothermal energy management. With advancing exploitation of geothermal resources deepens, precise understanding of this mechanism becomes paramount for devising effective reinjection strategies, optimizing reservoir utilization, and bolstering the economic viability of geothermal energy development. The article presents a comprehensive review of temperature field evolution across diverse heterogeneous thermal reservoirs under low-temperature tailwater reinjection conditions, and analyzes key factors influencing this evolution. It evaluates existing research methods, highlighting their strengths and limitations. The study identifies gaps in the application of rock seepage and heat transfer theories on a large scale, alongside the need for enhanced accuracy in field test results, particularly regarding computational efficiency of fractured thermal reservoir models under multi-well reinjection conditions. To address these shortcomings, the study proposes conducting large-scale rock seepage and heat transfer experiments, coupled with multi-tracer techniques for field testing, aimed at optimizing fractured thermal reservoir models' computational efficiency under multi-well reinjection conditions. Additionally, it suggests integrating deep learning methods into research endeavors. These initiatives are of significance in deepening the understanding of the evolution process of the temperature field in deep thermal reservoirs and enhancing the sustainability of deep geothermal resource development.
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Figure 1. Temperature field distribution of thermal reservoir in pore sandstone at different time (a) t=0.05a,(b) t=1a, (c) t=10a, (d) t=30a (Wang et al. 2023)
Figure 2. Temperature field distribution of fractured carbonate thermal reservoir at different time (a) t=1a, (b) t=10a, (c) t=15a, (d) t=20a (Wang et al. 2021)
Figure 3. Relationship between different fracture openings and average reservoir temperature (Li et al. 2019)
Figure 4. Average temperature of thermal reservoir on the mining side with different irrigation and irrigation spacing (Wang et al. 2021)
Figure 5. Mining side temperature at different reinjection pressure (Wang et al. 2021)
Figure 6. Mining side temperature at different reinjection temperature (Wang et al. 2021)
Figure 7. Experimental apparatus for seepage and heat transfer (Huang et al. 2021)
Table 1. Formula for convective heat transfer coefficient of rocks
Author Types of thermal storage rocks Convective heat transfer formula Zhao (2014) Granite $ h=\dfrac{-\mathrm{l}\mathrm{n}\dfrac{{T}_{2}-{T}_{c}}{{T}_{1}-{T}_{c}}{\rho }_{w}{c}_{p,w}u\delta \dfrac{{K}_{r}}{2}}{\mathrm{l}\mathrm{n}\dfrac{{T}_{2}-{T}_{c}}{{T}_{1}-{T}_{c}}{\rho }_{w}{c}_{p,w}u\delta \dfrac{d}{4}+{K}_{r}L} $ Zhang (2014) Granite $ h=\dfrac{21.16{c}_{p}\lambda \rho u\delta ({T}_{2}-{T}_{1})}{42.32\lambda L{T}_{0}-2\pi {c}_{p}{r}_{0}\rho u\delta ({T}_{2}-{T}_{1})-21.16\lambda L({T}_{1}+{T}_{2})} $ Bai et al. (2016) Granite $ h=\dfrac{{c}_{p,w}{\rho }_{w}u\delta ({T}_{2}-{T}_{1})}{2L\left({T}_{c}-\dfrac{{c}_{p,w}{\rho }_{w}u\delta ({T}_{2}-{T}_{1})}{42.32{K}_{r}L}-\dfrac{{T}_{1}+{T}_{2}}{2}\right)} $ Li et al. (2017) Granite $ h=\dfrac{{c}_{\rho ,w}m({T}_{2}-{T}_{1})}{S({T}_{s}-{T}_{f})} $ Luo et al. (2019） Granite $ h=\dfrac{{c}_{p,w}{\rho }_{w}{q}_{v}({T}_{2}-{T}_{1})}{dL\left({T}_{c}-\dfrac{{T}_{1}+{T}_{2}}{2}\right)} $ Zhan (2021) Carbonate rock $ h=\dfrac{{c}_{w}{\rho }_{w}{q}_{w}({T}_{w2}-{T}_{w1})}{2LR\left[\dfrac{1}{2}\left({T}_{i1}+{T}_{i2})+{T}_{c}-({T}_{w1}+{T}_{w2}\right)\right]} $ 
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Table 2. Classification of common tracers
Category Common tracers Advantage Shortage Natural tracer Hydrogen and oxygen isotopes, noble gases, 222Rn — — Inert tracers NaCl, KCl, NaBr, KBr, Ki. el. halide Good low temperature stability and easy to detect The background value in storage is high, adsorption, and the reaction occurs at high temperature. Fluorescein, sodium naphthalenesulfonate, rhodamine, saffron Economical and relatively non-toxic, low value in thermal reservoir, easy to detect Sensitive to pH, salinity and high chloride concentration, photochemical and chemical decay, susceptible to sediment adsorption, decomposition at high temperatures and loss of fluorescence properties. Radioactive tracers 3H,35S,82Br,131I High sensitivity, strong anti-interference ability, easy to detect The test requirements are high and easy to pollute the environment. Reaction tracer Esters, amides, carbamates The heat exchange area and temperature drop rate can be determined Data analysis requires experimental correction factors, which increases the workload. Smart tracer Nanoparticles, quantum dots Economical, environmentally friendly, stable, easy to detect, designed as a conservative or reaction tracer as needed —
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Table 3. Common numerical simulation software and functions
Software name Algorithm Multi-field coupling capability COMSOL Finite elements THMC OPENGEOSYS Finite elements THMC FLUENT Limited volume THM TOUGH-FLAC Finite difference THM FRACMAN Finite elements THM
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