Numerical Investigation of Techno-Economic Multiobjective Optimization of Geothermal Water Reservoir Development: A Case Study of China
Abstract
:1. Introduction
2. Target Project
2.1. Background
2.2. Geothermal Heating Strategy
3. Mathematical Model and Optimization Methodology
3.1. Thermal–Hydraulic–Mechanical Model
- (1)
- As the microstructures are well connected, thus, the hydraulic and transport characteristics of the rock matrix can be described by averaged quantities.
- (2)
- Local thermodynamic equilibrium is assumed between liquid phase and solid phase.
- (3)
- There is diffusion, convective and conductive heat transfer in porous media. Radiation heat transfer, which has little effect on geothermal water, is ignored.
- (4)
- As total fluid balance (not transported species) and yields are considered in the model, there is no water loss in the geothermal reservoir.
3.2. Calculation of Well Number based on Heating Load
3.3. Multiobjective Optimization Model for Heating Systems
3.4. Simulation Procedure
4. Result and Discussion
4.1. Sensitivity Analysis
4.1.1. Effect of Well Spacing
4.1.2. Effect of Reinjection Temperature
4.1.3. Effect of Production Rate
4.2. Numerical Optimization
4.2.1. Parameter Optimization
4.2.2. Economic Optimization of Geothermal System
5. Conclusions
- Mathematical models of geothermal development with direct and indirect geothermal district heating systems are established. Furthermore, the optimal well number, well spacing, production rate, and reinjection temperature can be obtained to meet engineering, environmental, and economic criteria.
- Well spacing, reinjection temperature, and production rate are the most significant parameters affecting thermal breakthrough in geothermal reservoirs. With decreased well spacing and reinjection temperature or increased production rate, premature thermal breakthrough would occur, resulting in low efficiency of geothermal heating systems.
- For indirect geothermal district heating systems, the construction investment of geothermal wells is reduced by up to 20%, and annual water consumption is reduced by up to 50%, but electricity consumption costs increase by 5% to 30%.
- Well number and reinjection temperature have a huge effect on the payback period of investment for geothermal development. The payback period of direct geothermal district heating systems is longer than that of indirect systems. Indirect systems have a good environmental adaptability and profitability which is more suitable for building heating.
- In the case of Xinji, China, an indirect geothermal district heating system is much better than a direct geothermal district heating system, both technically and economically. The optimal production rate, reinjection temperature, and well spacing for 50 years of building heating are 100 m3/h, 301.15 K, and 300 m, respectively. This optimal production parameters can be the reference for the design of geothermal heating systems in other regions. The systematical calculation approach can be reasonably applied to the selection and optimization of other geothermal systems.
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Property | Value | Property | Value | Property | Value |
---|---|---|---|---|---|
Geothermal scale | 266.6 km2 | Specific heat capacity of water | 4185.4 J/kg·K | Thermal conductivity of water | 0.6 W/(m·K) |
Thickness of reservoir | 512 m | Porosity | 30% | Thermal conductivity of rock | 3 W/(m·K) |
Storage temperature | 333.15 K | Rock density | 2600 kg/m3 | Poisson’s ratio of rock | 0.1 |
Storage pressure | 1300 Pa | Rock heat | 878 J/kg·K | Young’s modulus of rock | 10 Gpa |
Water density | 967.4 kg/m3 | Water compressibility | 4.5 × 10−10 Pa−1 | Permeability | 160 mD |
Water viscosity | 4.15 × 10−6 m2/s | Rock compressibility | 4.3 × 10−10 Pa−1 | Well spacing | 200 m |
Type | Reinjection Temperature | Production Rate | Minimum Well Spacing |
---|---|---|---|
DGDHS | 308.15 K | 80 m3/h | 250 m |
100 m3/h | 300 m | ||
120 m3/h | 350 m | ||
IGDHS | 301.15 K | 80 m3/h | 300 m |
100 m3/h | 300 m | ||
120 m3/h | 400 m | ||
IGDHS | 288.15 k | 80 m3/h | 350 m |
100 m3/h | 400 m | ||
120 m3/h | 450 m |
Construction Cost | Value | Operating Cost | Value | Revenue | Value |
---|---|---|---|---|---|
Vertical well materials expenses | 199 USD/m | Electricity | 0.078 USD/kWh | Residential warm fee | 2.8 USD/m2 |
Directional well materials expenses | 208 USD/m | Water | 0.65 USD/t | Commercial warm fee | 3.9 USD/m2 |
Surface ancillary works | 32.3 k USD | Maintenance | 50 K USD/year | ||
Geothermal well construction cost | 13 USD/m | Salaries | 900 USD/month/person | ||
Other expenses | 2.99 USD/m2 |
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Zhang, L.; Wang, R.; Song, H.; Xie, H.; Fan, H.; Sun, P.; Du, L. Numerical Investigation of Techno-Economic Multiobjective Optimization of Geothermal Water Reservoir Development: A Case Study of China. Water 2019, 11, 2323. https://doi.org/10.3390/w11112323
Zhang L, Wang R, Song H, Xie H, Fan H, Sun P, Du L. Numerical Investigation of Techno-Economic Multiobjective Optimization of Geothermal Water Reservoir Development: A Case Study of China. Water. 2019; 11(11):2323. https://doi.org/10.3390/w11112323
Chicago/Turabian StyleZhang, Luyi, Ruifei Wang, Hongqing Song, Hui Xie, Huifang Fan, Pengguang Sun, and Li Du. 2019. "Numerical Investigation of Techno-Economic Multiobjective Optimization of Geothermal Water Reservoir Development: A Case Study of China" Water 11, no. 11: 2323. https://doi.org/10.3390/w11112323