Geothermal energy is one of the most potential renewable energy resources. How to efficiently extract and utilize geothermal energy has been a worldwide hot topic. Co-axial closed-loop geothermal system is a novel method using a continuously closed wellbore without water exchange with. It is more suitable for reservoirs with medium or low temperature and permeability because many problems could be avoided such as lack of in situ groundwater or low infectivity of the reservoir. Many companies and research institutes have applied closed-loop geothermal system in building heating engineering and some fine results have been gained. However, in practical engineering construction, the area of a closed-loop geothermal system heating system is a very important parameter. It directly determines the cost accounting and initial design of the project. Accurate and reliable estimation of heating capacity becomes very important. In this study, a wellbore–reservoir coupling model is established, which is calibrated using measured data from a short-term field trial operation. We have carried out mixed convective–conductive fluid-flow modeling using a wellbore flow model for TOUGH2 called T2Well to investigate the heat extraction performance of closed-loop geothermal system. The system evolution and the effect of flow rate and injection temperature on heat production performance are discussed. The result shows that the intermittent production cycles are more beneficial for heat extraction and system maintenance, and the temperature recovery between two heating seasons is enough to maintain system heating. And we can calculate that a geothermal well can ensure heating of buildings of 10,000–20,000 m² and the heating area of intermittent operation is 4000 m² more than continuous operation. Besides, the sensitivity analysis of parameters is also carried out.

The placement of a well doublet plays a significant role in geothermal resource sustainable production. The normal well placement optimization method of numerical simulation-based faces a higher computational load with the increasing precision demand. This study proposes a surrogate model-based optimization approach that searches the economically optimal injection well location using the Grey Wolf Optimizer (GWO). The surrogate models trained by the novel Multi-layer Regularized Long Short-Term Memory–Convolution Neural Network concatenation model (MR LSTM-CNN) will relieve the computation load and save the simulation time during the simulation–optimization process. The results showed that surrogate models in a homogenous reservoir and heterogenous reservoir can predict the pressure–temperature evolution time series with the accuracy of 99.80% and 94.03%. Additionally, the optimization result fitted the real economic cost distribution in both reservoir situations. Further comparison figured out that the regularization and convolution process help the Long Short-Term Memory neural network (LSTM) perform better overall than random forest. And GWO owned faster search speed and higher optimization quality than a widely used Genetic Algorithm (GA). The surrogate model-based approach shows the good performance of MR LSTM-CNN and the feasibility in the well placement optimization of GWO, which provides a reliable reference for future study and engineering practice.

In this study, precipitation, and temperature data from HadGEM2-ES under Representative Concentration Pathways (RCPs) 4.5 and 8.5 were used to evaluate drought in China in the 21st century. The K-means clustering algorithm was used to analyze the regional characteristics of the dry hazard index (DHI) in China, and the impact of climate change on the variation trend and periodicity of regional drought in China was explored. The results show that the temperature and potential evapotranspiration (PET) of all clusters have an increasing trend under the two RCPs, and the precipitation of most clusters shows a significantly increasing trend. The drought index calculated by the standardized precipitation-evapotranspiration index (SPEI) is higher than those calculated by the standardized precipitation index (SPI) and standardized effective precipitation evapotranspiration index (SP*ETI). The variation trends of drought intensity and frequency in China are not significant in the 21st century; however, the local variation trends are significant. The droughts in most parts of the Xinjiang Province, northern Tibet and western Qinghai Province show significantly increasing trends. According to the DHI analyses and the variations in the drought area ratio, with increases in greenhouse gas concentrations, the droughts in central and western China will become more severe, and drought will spread to the eastern areas of China. In the case that both precipitation and temperature may increase in the future, the increase in evapotranspiration caused by temperature rise will greatly affect drought dynamics. The main drought periodicity in China in the 21st century is 1~3.6 years. Drought is affected by climate change but not significantly.

Geothermal energy is clean and independent to the weather and seasonal changes. In China, the huge demanding of clean energy requires the geothermal energy exploitation in the reservoir with depth larger than 1000 m. Before the exploitation, it is necessary to estimate the potential geothermal energy production from deep reservoirs by numerical modeling, which provides an efficient tool for testing alternative scenarios of exploitation. We here numerically assess the energy production in a liquid-dominated middle-temperature geothermal reservoir in the city of Tianjin, China, where the heat and fluid transport in the heterogeneous reservoir and deep wellbores are calculated. It is concluded that the optimal injection/production rate of the typical geothermal doublet well system is 450 m³/h, with the distance between geothermal doublet wells of 850 m. The outflow temperature and heat extraction rate can reach 112°C and 43.5 MW, respectively. Through decreasing injection/production rate lower than 450 m³/h and optimizing layout of the injection well and production well (avoiding the high permeability zone at the interwell sector), the risk of heat breakthrough can be reduced. If the low permeability zone in the reservoir is around injection well, it usually leads to abnormal high wellhead pressure, which may be solved by stimulation technique to realize stable operation. The methodology employed in this paper can be a reference for a double-well exploitation project with similar conditions.

Shallow geothermal energy is stable and clean. Using a heat pump to produce groundwater and realize heating and cooling can effectively prevent haze and reduce energy consumption. To reduce engineering costs, many buildings in Beijing, China, plan to utilize single-well groundwater source heat pumps. Numerical modeling is an effective way to gain an understanding of thermal transport processes. However, wellbore-reservoir coupling and the uncertainty of productivity due to geological parameters make simulation difficult. A wellbore-reservoir-integrated fluid and heat transport model is defined by T2Well simulator to predict the productivity of a typical single-well system, with consideration of complex geological factors. The model is validated by the analytical model developed in Beijing, China. The fluid processes in the wellbore are described by 1 D non-Darcy flow, and the reservoir 3 D fluid and heat transport processes are calculated. Six crucial factors satisfying a random distribution are used, and for a single well that can supply heat for an area of 9000 m², the output temperature during the heating season ranges from 11°C to 15°C.

The well-doublet production model has far-reaching implications for the sustainable utilization of geothermal resources. The position of the injection well in the geothermal production process is closely connected to the emergence of thermal breakthroughs and the production lifespan. Thus, it is necessary to optimize the well placement. However, traditional simulation and optimization approaches require a long time and have a high computing burden. In this paper, a surrogate model based on the back propagation neural network (BPNN) is trained to improve the drawbacks of previous approaches, and it is combined with the genetic algorithm (GA) to develop a simulation-optimization approach to find the optimal well placement of a well-doublet geothermal production system. To guarantee that the training data have appropriate physical significance, the TOUGH2 program is used for the hydro–thermal model development of the geothermal reservoir of the Minghuazhen Formation in Tianjin, China. A sensitivity analysis is used to select the series of samples used for training, which includes temperature and pressure variation, heat extraction rate (Wh), and economic cost. The results reveal that the surrogate model has excellent prediction accuracy and efficiency for physical processes, and the genetic algorithm optimization outcomes are consistent with predictions, which is of practical importance.