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The electricity required for air conditioning is soaring worldwide. Absorption chillers represent an alternative to classical vapor compression systems, using heat instead of electricity. However, absorption chillers powered by renewable geothermal heat have received little attention so far. This paper introduces a system using a hot geothermal fluid (typically in the range of 80–110 °C) to produce cooling through a single-effect absorption chiller, and Domestic Hot Water (DHW) through a heat exchanger. It considers a hotel located in Martinique, a French Caribbean island. The electric consumption of every subsystem has been thoroughly estimated. The originality of this paper is twice: i) the system is modelled in TRNSYS software considering dynamic conditions, ii) the system undergoes surrogate modelling and multi-objective optimization to minimize both the cost and the CO 2 content of the delivered thermal energy. Several scenarios are considered, depending upon the geothermal temperature, mass flow rate, well remoteness, and demand size. The studied system appears to be systematically more expensive than a combination of a classical vapor compression chiller and a boiler for DHW. However, it can significantly decrease the CO 2 content of the provided energy, especially in an island where most electricity is generated from fossil fuels. The proximity of the geothermal well and the use of the warm water leaving the absorption generator (here for DHW production) appear to be key factors for system relevance, along with a hotter geothermal fluid (e.g., 110 °C instead of 80 °C). • A geothermal absorption chiller in tropical climate is modelled. • The influence of design parameters on electricity consumption is investigated. • The system undergoes multi-objective optimization. • The well proximity and geothermal temperature are keys to the system relevance. [ABSTRACT FROM AUTHOR]

As global efforts intensify to mitigate climate change through Net-Zero by 2050, the transition from fossil fuels to renewable energy sources is of utmost importance. Within this context, geothermal power generation is an underestimated yet rapidly developing field with immense potential. A notable opportunity arises from repurposing abandoned oil and gas wells (AOGWs) as alternative sources for geothermal power production. This study employs multi-criteria decision analysis (MCDA) to examine the techno-sustainability of four geothermal power systems: i) a business-as-usual geothermal power plant (GEO bau), ii) repurpose two completely AOGWs for geothermal power generation (R-GEO double), iii) repurpose a single completely AOGW (R-GEO single), iv) repurpose semi-AOGWs (i.e., wells still in operation but with high water cut, R-GEO semi). We assessed 30 criteria across technical, environmental, economic, and social dimensions via the analytical hierarchy process. The study identifies R-GEO semi as the most techno-sustainable option due to its superior performance across environmental, economic, and social dimensions. However, GEO bau closely follows R-GEO semi , securing its position as the second-best option, marked by its outstanding technical proficiency and robust environmental and social performance. Conversely, R-GEO double and R-GEO single show limited techno-sustainability competitiveness. When sensitivity analyses are applied to the weighting factors, R-GEO semi demonstrates resilience and remains as the best option in most cases. However, GEO bau could outperform R-GEO semi when the value of technical criteria is increased (weightings >0.255) or when the weightings for environmental or economic aspects decrease (weightings <0.236 and < 0.244 respectively). R-GEO double and R-GEO single only overtake GEO bau when the economic aspect reaches a weighting of 0.659 or more. Despite R-GEO semi being the most promising option, it faces challenges due to limited power generation capacity and the availability of wells approaching their end-of-lives. The findings underline the necessity for broader stakeholder input, inclusion of more technical and social criteria, and data-driven decision-making processes. • First multi-criteria decision analysis of repurposing abandoned wells for geothermal power. • Repurposed semi-abandoned oil & gas wells are the most techno-sustainable option. • Technical criteria can tilt performance favour towards non-repurposed systems. • Drilling activity should be minimised to boost the deployment of repurposed systems. • Repurposed systems excel when economic criteria are prioritised. [ABSTRACT FROM AUTHOR]

• Different geothermal technology and their advancement are summarized. • The environmental impact of different geothermal technology is presented. • The advantages and disadvantages of the technology are presented. • The opportunities for improvement the geothermal technology are explored. • The potential role of the geothermal technology in the sustainable future is discussed. Rapid population growth as well as modern technology reliance lead to a greater demand for energy consumption. An ever growing focus on creating sustainable environment requires energy sources to be used with caution. Two effective solutions to address these concerns include utilizing renewable energy resources and increasing efficiencies of current technologies. Geothermal energy provides a renewable energy source that has potential to supply reasonable amounts of electricity, heating, and cooling. The present research elaborates upon methods of harnessing energy using various geothermal technologies. Various methods of performance improvement, as well as integration of geothermal technology with other renewable energy sources are also discussed. The environmental impact and economic viability of the technology are mapped as well. The advantages and disadvantages of the technology and opportunities for improvement are explored based on the recent studies. Briefly, the potential role of geothermal technology in a sustainable future is discussed in the study. Finally, the prospective topics of future research are presented for further investigation. [ABSTRACT FROM AUTHOR]

• Repurposing abandoned wells for geothermal utilization increases its deployment. • Bottomhole temperature is a detrimental factor to assess the viability of a geothermal system. • An operating full well conversion project to be implemented. • Repurposing abandoned wells holds significant economic and environmental advantages. • Assessment on a per-well basis needs to be performed. This review focuses on repurposing abandoned hydrocarbon (oil and gas) wells for the geothermal system development. There are approximately three million abandoned wells in the United States alone, and these wells emit alarming amounts of greenhouse gases and pose serious health hazards. These issues can be addressed by repurposing abandoned wells for the utilization of geothermal energy systems, leading to an increase in the number of geothermal systems deployments. Despite this potential, there are only a few numbers of installations of a geothermal system converted from hydrocarbon wells. Therefore, this study presents a comprehensive review on the current status of abandoned wells, potential risks to environment and human health they pose, and available feasibility and pilot studies on repurposing oil and gas wells for geothermal system development. The challenges, opportunities, and pathways to improve the current understanding of repurposing wells are presented in detail. Utilization of existing wells for an advanced geothermal energy storage system development for flexible energy generation is proposed. [ABSTRACT FROM AUTHOR]

Predicting the temperature, pressure, and permeability at depth is crucial for understanding natural-state geothermal systems. As direct observations of these quantities are limited to well locations, a reliable methodology that predicts the spatial distribution of the quantities from well observations is required. In this study, we developed a physics-informed neural network (PINN), which constrains predictions to satisfy conservation of mass and energy, for predicting spatial distributions of temperature, pressure, and permeability of natural-state hydrothermal systems. We assessed the characteristics of the proposed method by applying it to 2D synthetic models of geothermal systems. Our results showed that the PINN outperformed the conventional neural network in terms of prediction accuracy. Among the PINN-predicted quantities, the errors in the predicted temperatures in the unexplored regions were significantly reduced. Furthermore, we confirmed that the predictions decreased the loss of the conservation laws. Thus, our PINN approach guarantees physical plausibility, which has been impossible using existing machine learning approaches. As permeability investigations in geothermal wells are often limited, we also demonstrate that the resistivity model obtained using the magnetotelluric method is effective in supplementing permeability observations and improving its prediction accuracy. This study demonstrated for the first time the usefulness of the PINN to a geothermal energy problem. • A physics-informed neural net (PINN) for geothermics is proposed for the first time. • PINN predicts temperatures, pressures, and permeabilities in hydrothermal systems. • PINN outperformed conventional neural networks in terms of prediction accuracy. • PINN enhances physical validity of the predictions by considering conservation laws. • PINN is useful for geothermal inverse modeling by combining data and physics laws. [ABSTRACT FROM AUTHOR]

• A LSTM & MLP combinational network is proposed to predict geothermal productivity. • MLP is trained to learn non-linear relationship between productivity & constraints. • LSTM is used to memorize sequential relations within historical production data. • LSTM & MLP combinational network shows the best productivity prediction performance. Geothermal energy is one of renewable and clean energy resources. Predicting geothermal productivity is an essential task for managing a continuable geothermal system, which is a huge challenge due to the highly non-linear relationship between the productivity and constraint conditions, such as reservoir properties and operational conditions. Using numerical simulation to predict the geothermal productivity is computationally expensive and very time consuming. Therefore, this study proposes a novel Long Short-Term Memory (LSTM) and Multi-Layer Perceptron (MLP) combinational neural network to effectively forecast the geothermal productivity considering constraint conditions. In the LSTM and MLP combinational neural network, MLP is trained to learn the non-linear relationship between the geothermal productivity and constraint conditions, while LSTM is used to memorize sequential relations within the production data. We comprehensively evaluate the geothermal productivity prediction performance of the LSTM and MLP combinational network. It indicates that the LSTM and MLP combinational neural network could accurately and stably predict the geothermal productivity and has a good generalization ability. Compared with original LSTM, MLP, Simple Recurrent Neural Network (RNN), the LSTM and MLP combinational network demonstrates the best geothermal productivity prediction accuracy, stability and generalization ability. This study provides a high precision and efficiency forecasting method for the geothermal productivity prediction. [ABSTRACT FROM AUTHOR]

• Thermal, hydraulic and geochemical properties are assessed in a geothermal system. • High thermal conductivity formations are analyzed for thermal anomalies. • Potential power production is calculated. • The potential for lithium production is considered. Low-temperature geothermal power plants have great potential to provide baseload electricity worldwide. However, the technology is new and untested, and the systems require a significant financial investment. Site-specific feasibility studies are required to draw together available data and inform developers of production potential and challenges. This study applies coupled thermal, hydraulic, and geochemical modeling to characterize a horizontal geothermal well system in the Williston Basin. This study targeted thermal anomalies overlying 370 m thick halite and dolomite formations from the Devonian period. The halite and dolomite produced a 6°C thermal anomaly, or 16°C km−1 increase in the thermal gradient. A >5.0 MW power plant was achieved when targeting the Dawson Bay formation. A fractured, horizontal well in the Bakken formation was re-entered for injection, providing a savings of up to 40% of drilling costs. The geochemical analysis allowed for the calculation of potential precipitates in the production casing, and consideration of mitigation methods. In addition, the co-production of lithium was identified as potentially profitable in the Dawson Bay Formation. Considering the available resources and assets provided unique data to inform the development of a geothermal system in the Williston Basin. [ABSTRACT FROM AUTHOR]

• Heating mode is considered in numerical simulation and multi-objective optimization. • Regulated systems consume 50% less water and 25% more electricity than unregulated systems do. • The regulated systems have low construction cost with stable operation. • The regulated geothermal heating system could be applied in other regions. Geothermal heating technology is an important technique for alleviation of energy crisis and environmental pollution. A numerical simulation and multi-objective optimisation for geothermal heating systems are conducted considering factors such as the heating load, heating modes, and region characteristics. Heating modes play a vital role in the utilization of geothermal reservoirs. It is found that regulated geothermal heating system can postpone the formation of thermal breakthroughs and shrink radius of the cold front. It's also indicated that well spacing and production rate are the two main factors affecting the production performance and emission reduction of geothermal heating systems. Construction investment of geothermal wells and the annual water consumption of regulated geothermal heating system decreases by up to 30% and 60%, comparing with unregulated geothermal heating system, although electricity costs increase by 5% to 25%. Multi-objective optimized heating system parameters for Qingfeng project in China are achieved. The best regulated geothermal heating system with well spacing of 295 m and production rate of 100 m3/h generates the highest efficiencies in terms of heat production, emission reduction, and economic performance. The mathematical model and multi-objective optimization model can provide references for utilization of geothermal heating technology in other places with similar conditions. [ABSTRACT FROM AUTHOR]

• A Heat extraction test in a geothermal well was performed with a coaxial open loop. • Comparing to the closed loop, the proposed open loop can obtain greater heat output. • The outlet temperature can reach stability more quickly. • A simple conceptual model was proposed that matches well with the experimental data. We report a field heat extraction test from a deep geothermal well using a downhole coaxial open loop design in Tanggu, Tianjin China, which is different from most previous studies in that the well casing is closed to the geothermal reservoir. A downhole pump installed in the inner tube pumped the geothermal fluids to the surface. After releasing heat by flowing through a plate heat exchanger, the geothermal fluid returned to the aquifer completely and naturally by gravity through the annular section between the inner tube and the borehole wall. The proposed design realizes a sustainable and clean way of "no water withdrawn but heat only" in geothermal utilization. The experimental test well was used as a reinjection well for two years, and the maximum measured temperature in the well before the experiment was only 51 °C. The experimental results show that the proposed configuration worked very well in extracting heat from a deep geothermal reservoir. The system's stable heat output can reach approximately 275 kW after two weeks of continuous heat extraction, which was much larger than the theoretically calculated value for deep borehole heat exchangers by using the heat conduction model. [ABSTRACT FROM AUTHOR]