Geothermics最新文献

The EGS Collab project acquired continuous active-source seismic monitoring (CASSM) data before, during, and after hydraulic stimulations at the first testbed at the depth of 4850 ft (1478 m) at the Sanford Underground Research Facility in Lead, South Dakota, for monitoring fracture creation and evolution. CASSM acquisition was conducted using 24 hydrophones, 18 accelerometers, and 17 piezoelectric sources within four fracture-parallel wells and two orthogonal wells. 3D anisotropic traveltime tomography and anisotropic elastic-waveform inversion of the campaign cross-borehole seismic data show that the rock within the stimulation region is a heterogeneous horizontal transverse isotropic medium. We use these inversion results as the initial models and apply 3D anisotropic first-arrival traveltime tomography and 3D anisotropic elastic-waveform inversion to the CASSM data acquired after each stimulation in May, 2018 and December, 2018. We observe the spatiotemporal evolution of seismic velocities and anisotropic parameters caused by hydraulic fracture stimulations, showing the regions of rock alternation caused by hydraulic fracture stimulation.

The fracture-dominated convection heat transfer behavior is commonly involved in the development, utilization and storage of thermal energy in fractured rock engineering. An experimental system assembled by a peristaltic pump drive, a liquid preheater and an electric blast drying oven is developed to quantify the effect of fracture roughness on the convection heat transfer characteristics. The overall heat transfer coefficient (OHTC) and the amount of heat transfer quantity from six fracture samples with different inlet temperatures and flow rates are calculated by the data acquisition at five observation points. In general, the average convective heat transfer efficiency between water and rock decreases gradually with time, and then enters a stage of thermal equilibrium while the temperatures at the five observation points become constant. The increasing flow rate can lead to the gradual increase of the OHTC and the slowdown of its growth rate. The OHTC is negatively correlated with the inlet temperature. With the increase of fracture surface roughness, the dominant flow effect is significantly enhanced, which leads to the weakening of heat transfer characteristics and the gradual reduction of OHTC. Finally, the heat transfer quantity decreases with the increase of roughness, and exists an inflection point with the flow rate.

Geothermal power plants are among the most important renewable energy power plants owing to their high-capacity factors and integrated utilization possibilities. Currently, these power plants utilize geothermal fluid to generate electricity. Although their emissions are lower than those of conventional power plants, gasses such as CO₂ and H₂S are released into the air from the cooling towers, particularly in flash-type geothermal power plants

To reduce the emission of CO₂ gas released from geothermal power plants, reinjection studies have mainly been carried out around the world. These types of studies require extensive analysis of underground fracture systems, detailed geosciences, and the reservoir studies. However, these studies are considered risky and expensive for most plant operators because possible changes in underground fracture systems may affect the productivity of geothermal production zones. In terms of the environmental impact, hydrogen sulfide is a more harmful gas than CO₂. Effective H₂S removal methods cannot be widely used, except in areas with extremely high concentrations, because they commonly incur significant costs for plant operators. Effective H₂S removal methods are not widely available except for geothermal sites with high concentrations. The fact that local limit values can be exceeded in geothermal power plants with relatively low H₂S concentrations, such as geothermal power plants in Türkiye, pushes plant operators to find new low-cost solutions due to high operation costs. For this reason, a treatment method that can be applied at every site and whose cost is not too high has not yet been put forward. However, NaOH is used for this purpose in geothermal fields such as steam-dominated Geyser field to increase the pH values in geothermal wells, which has been producing for a long time.

In this study, field tests were carried out with five different chemicals and pure water to examine the reduction of non-condensable gasses in a geothermal power plant located in the Kızıldere (Denizli, Türkiye) geothermal field, one of the most important geothermal fields in the world. According to this, the capture of these gasses is technically possible using chemical methods, with a performance of up to 70 % observed in CO₂ gas capture.

However, although it is possible to capture 70 % of non-condensable gasses with such chemical methods, the consumable cost of the operation is quite high.

The Borehole Heat Exchanger (BHE) plays a pivotal role in enhancing heat exchange efficiency within Ground Source Heat Pump (GSHP) systems. The accurate prediction of the BHE's outlet fluid temperature is crucial for optimizing GSHP performance, energy storage, and resource conservation. However, conventional machine learning methods encounter challenges in manual feature extraction, learning complex nonlinear relationships, and adapting to real-world scenarios. To address these limitations, this research proposes a crossbreed model integrating Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) architectures to forecast long-term outlet fluid temperature in BHE systems. The model framework encompasses data preprocessing, utilizing refined data in the CNN module for temporal feature extraction, subsequently passed to the RNN module to capture sequential and temporal patterns from each dataset. Specifically, the advanced CNN-RNN architecture is designed to establish a comprehensive input-output mapping, leveraging essential input features such as inlet fluid, ambient air, and subsurface temperatures at varying depths (0, 10, and 20 m). Performance evaluation metrics, including R², RMSE, MAE, and AARE, are employed to compare and assess prediction accuracy across various models, including LSTM, CNN, and SimpleRNN. The obtained results demonstrate the superior performance of the proposed model, achieving an RSME of 0.818, MAE of 0.642, AARE of 0.0305, and an R² value of 98.75 %. This surpasses the performance of traditional prediction models (LSTM, CNN, and SimpleRNN) by 3.01 %, 5.80 %, and 19.52 %, respectively. Notably, the remarkably low MAE of 0.642 exhibited by a CNN-RNN model underscores its capability to outperform traditional approaches, especially when handling large datasets. These findings emphasize the significance of the developed model in facilitating efficient operation, positioning it as a valuable tool for advancing the long-term sustainability of BHE systems.

The geothermal resources and their connectivity with the deep tectonic are meaningful to understand through their structural geometry in the NW Himalayas. Therefore, over the NW-SE profiles, 23 audio Magnetotelluric (AMT) sites and over the SW-NE profile, 15 broadband Magnetotelluric (BBMT) sites were carefully chosen to provide the detailed structure. The electrical models were produced from a 2-D inversion algorithm based on the non-linear conjugate gradient method. The Chumathang-Mahe and Puga-Sumdo regions offer excellent geothermal potential as heat/fluid passes through the Mahe Fault (MF), Zildat Fault (ZF) and Kiagor Tso Fault (KTF). These faults are well revealed in the electrical models beneath the subsurface. The Puga Valley has an excellent geothermal resource at shallow depth. A ∼1000 m thick sulfide mineral body is defined with high conductivity (∼1 Ω-m). The crustal structure shows a highly conductive mid-crust beneath the Ladakh batholiths with no manifestation roots. The signs of the Indus-Tsangpo Suture Zone (ITSZ) and Shyok Suture Zone (SSZ) are well represented in the crustal model and are associated with the steep-dipping faults. The Chumathang-Mahe and Puga-Sumdo geothermal regions are connected with low resistivity body (C1) at shallow depths in the crustal model along the SW-NE profile and offer a path for deep fluid flow. The frictional heat generated in the process of the Indian plate subducting beneath the Tibetan plateau and the collision zone between India and Asia carried to melt. The high heat surface flow indicates the thermal origin of low resistivity. Thus, a ∼10 sq. km low resistivity reservoir is identified beneath the Tso Morari dome in the crustal model that overlies a resistivity feature. The high conductivity at mid-crust determined at active tectonic fabric may be potential geothermal resources yielding high well-productivity.

Thermal mineral water (temperature >25 °C) is a valuable resource, especially when it contains a certain amount of Li. In this study, an attempt was made to elucidate the factors affecting spatial distribution of lithium (Li) resources and interpret the formation mechanisms of thermal mineral waters in the Sichuan Basin. The spatial distribution, reservoir temperatures, and hydrochemical characteristics of thermal mineral waters were systematically analyzed in the Sichuan Basin, preliminarily revealing Li sources through hydrochemical methods. Results are showed as follows: (1) The Li-bearing (Li ≥ 1 mg/L) thermal mineral waters are mainly distributed in the southwestern part of the Sichuan Basin. There are three sites of thermal mineral waters with Li content greater than 25 mg/L with the hydrochemical type of Cl–Na type: Pengji Well (37.7 mg/L), Foguanghu Well (99.5 mg/L) and Zhougongshan New Well (118.0 mg/L). Among them, the Foguanghu Geothermal Well and the Zhougongshan New Well have high Li content and low Mg/Li (10.7 and 2.5), indicating great potential for utilization. (2) The Li-bearing thermal mineral waters are mainly formed in the fissure and pore reservoirs of the Middle–Lower Triassic. They are mainly affected by the dissolution of halite minerals and the deeply buried ancient seawater of the Triassic. The Li-bearing thermal mineral waters possess high contents of total dissolve solids (TDS) (4.3–230.0 g/L), Cl (0.7–142.9 g/L), Na (0.5–76.7 g/L), K (0.1–52.0 g/L), B (4.1–739.6 mg/L), and Li (1.5–118.0 mg/L). Moreover, the temperature of the geothermal reservoir ranges from 40 to 150 °C. The geothermal reservoir temperatures calculated by K–Mg and Mg–Li geothermometers are positively correlated with the Li content. (3) The Li source of the thermal mineral waters of the Sichuan Basin are dominated by multiple factors: first, Li concentration by evaporation, concentration and deep-seated metamorphism of ancient Triassic seawater; second, water–rock interaction between deep thermal mineral waters and mung bean rock or Emeishan basalt; and third, active tectonic activity and deep faulting induced upward flow of Li in deep fluids. This research enhances understanding of the formation of thermal mineral waters in the Sichuan Basin, offering scientific basis for the exploitation of Li resource.

Polycrystalline diamond compact (PDC) bits are superior for drilling geothermal wells because of their superior drilling performance compared to conventional roller cone bits. However, the shear action of PDC bits generates detrimental vibrations during drilling. The main objective of this study was to establish a methodology to analyze and predict the stick-slip severity in hard rocks for geothermal wells. Two non-linear coupled axial-torsional bit-rock interaction (BRI) models are presented: one is based on a velocity-decaying friction model (VDF), and the other is based on a state-dependent delay friction model (SDDF). The capabilities of the two models were evaluated to assess the axial and torsional dynamic stabilities of drill stems in deep geothermal wells. The comparative analysis, along with the results from both models, were validated using geothermal well downhole data. Five distinct zones were selected for analysis, and the stick-slip severity value (SSV) was calculated using these two models (VDF and SDDF). The results from these two models for the five different zones were compared with the field data. The results indicated that VDF demonstrated superior quality when compared with field values, as the results of VDF were within the interquartile range of the observed SSV in each zone. A sensitivity analysis employing spider plots was performed for both models, considering parameters related to rock, bit, operational, and frictional aspects. In terms of the operational parameters, the weight-on-bit (WOB) and revolutions per minute (RPM) exerted the most significant influence on the SSV for both models. For the VDF model, the sensitivity analysis indicated that the frictional parameter, uniaxial compressive strength (UCS), and number of cutters (NOC) had the most pronounced impact on the SSV. In the case of the SDDF, the Intrinsic specific energy (ISE), bit diameter, and number of blades (NOB) are the key factors that predominantly affect the SSV.

The commercial viability of low-temperature geothermal basins is still a major stumbling block for field implementation of geothermal projects in India. Though there are several low-temperature basins, the presence of high-temperature gradient in few regions of India led to the hypothesis of testing the advanced geothermal potential in India. Therefore, this study holistically investigates the prospect of superhot rock geothermal potential in India vis-à-vis the techno-economical prospect and reservoir characteristics. Additionally, a comparative study is conducted with respect to the conventional geothermal system in the Son-Narmada-Tapti field to understand the effects of technical and economic parameters.

The geothermal provinces in India can be utilized to reduce the use of fossil fuels for direct use and heating applications. However, geothermal resources suffer a setback due to high capital cost and risks associated with the feasibility of extraction process of heat energy. This study investigates the possibility of a superhot rock site to drive down the cost. To assess the technical feasibility, a comprehensive study is conducted using the reservoir heat and flow simulation for the proposed site. The study is conducted using the commercial package to obtain the heat energy that can be generated over the thirty years of injection and production. The feasibility analysis cannot be complete without the study of economic aspects. Hence, the market cost of electricity (LCOE) is estimated to understand the economic feasibility of the proposed superhot rock project. To get a better insight into the superhot rock potential, the resource potential and economic feasibility is compared with the similar system for the conventional geothermal prospect.

The reservoir simulation study suggests that the dual superhot rock well system has capacity to provide 170 – 175 MWe in the region which is much higher than that of the conventional system (12 – 15 MWe). Also, it was observed that the dual well geothermal system, the waterfront does not reach the producer well over the thirty years of cold-water injection in both systems. The economic feasibility study shows that the levelized cost of electricity is most likely to be $67/MWh which is significantly less than the conventional geothermal system ($122/MWh).

Accurately determining the geothermal gradient is crucial for assessing geothermal energy potential. In Colombia, despite an abundance of theoretical geothermal resources, large regions of the country lack gradient measurements. This study introduces a machine learning approach to estimate the geothermal gradient in regions where only global-scale geophysical datasets and course geological knowledge are available. We find that a Gradient-Boosted Regression Tree algorithm yields optimal predictions and extensively validates the trained model, obtaining predictions of our model within 12% accuracy. Finally, we present a geothermal gradient map of Colombia that serve as an indicator of potential regions for further exploration and data collection. This map displays gradient values ranging from 16.75 to 41.20 °C/km and shows significant agreement with geological indicators of geothermal activity, such as faults and thermal manifestations. Additionally, our results are consistent with independent findings from other researchers in specific regions, which supports the reliability of our approach.