Newest GEG Papers
Refereed journal papers accepted the last 6 months
Underlined names are links to current or past GEG members
Paving the way for CO2-Plume Geothermal (CPG) systems: A perspective on the CO2 surface equipment
Schifflechner, C.,
J. de Reus,
S. Schuster,
A. Corpancho Villasana,
D. Brillert,
M.O. Saar, and
H. Spliethoff,
Energy, 305, 2024. https://doi.org/10.1016/j.energy.2024.132258 [Download] [View Abstract]Subsurface reservoirs play an important role in decarbonizing the energy sector, be it through geothermal energy production or carbon capture and storage. In recent years, there has been an increasing interest in CO2-Plume Geothermal systems, which combine carbon sequestration with geothermal, using CO2 instead
of water as a subsurface heat and pressure energy carrier. Since CO2-Plume Geothermal systems are added to full-scale CO2 Capture and Sequestration operations, all of the initially injected CO2 is ultimately stored. CO2-Plume Geothermal, therefore constitutes of both CO2 Capture Utilization as well as Storage. This paper assesses the huge technical potential of this technology, identifying a potentially highly relevant market for CO2 equipment manufacturers and discusses the current research demand, based on the current state of the art of CO2 equipment. Both temperature and pressure levels are significantly lower than CO2 turbine designs investigated and proposed so far for other applications, such as waste heat recovery. For a depth of 5 km, a typical one-stage radial turbine design might have a rotational speed of 23’000 rpm to 42’000 rpm and an impeller diameter between 96 mm to 155 mm. Together with technology-specific requirements, due to produced fluid impurities, it becomes evident that significant further development efforts are still necessary. (Paper accepted 2204-06-28)
GeoProp: A thermophysical property modelling framework for single and two-phase geothermal geofluids
Merbecks, T.,
A.M.M. Leal,
P. Bombarda,
P. Silva,
D. Alfani, and
M.O. Saar,
Geothermics, pp. 103146, (in press). https://doi.org/10.1016/j.geothermics.2024.103146 [Download] [View Abstract]The techno-economic evaluation of geothermal resources requires knowledge of the geofluid's thermophysical properties. While the properties of pure water and some specific brines have been studied extensively, no universally applicable model currently exists. This can result in a considerable degree of uncertainty as to how different geothermal resources will perform in practice. Geofluid modelling has historically been focused on two research fields: 1) partitioning the geofluid into separate phases, and 2) the estimation of the phases’ thermophysical properties. Models for the two fields have commonly been developed separately. Recognising their potential synergy, we introduce GeoProp, a novel geofluid modelling framework, which addresses this application gap by coupling existing state-of-the-art fluid partitioning simulators, such as Reaktoro, with high-accuracy thermophysical fluid property computation engines, like CoolProp and ThermoFun. GeoProp has been validated against field experimental data as well as existing models for some incompressible binary fluids. We corroborate GeoProp's efficacy at modelling the thermophysical properties of geothermal geofluids via a case study on the heat content of different geofluids. Our results highlight the importance of accurately characterising the thermophysical properties of geofluids in order to quantify the resource potential and optimise the design of geothermal power plants. (Paper accepted 2024-08-16)
Innovative temperature-sensitive phase-transition fracturing: Boosting unconventional resource development
Zhang, N.,
L. Zhao,
L. Jiang,
Y. Ju,
Z. Luo,
P. Liu,
Y. Pei,
K. Wen,
W. Liu, and
et et ,
The Innovation Energy, 1, pp. 100039-100040, 2024. https://doi.org/10.59717/j.xinn-energy.2024.100039 [Download] [View Abstract]China's energy structure is primarily based on coal, oil, and natural gas, with a recent rise in the use of natural gas and renewable energy. Oil accounts for approximately 17.9% of energy consumption, while natural gas makes up about 8.4%. Although shale oil, gas, and other unconventional resources constitute a minor part of China's energy mix, their development is crucial. Exploiting these resources can reduce dependence on imported oil and gas, enhance energy supply diversity and stability. This strategy will not only secure energy but also promote sustainable economic development and environmental improvements. (Paper accepted 2024-08-05)
Investigation of the filling of a pore body with a nonwetting fluid: a modeling approach and Computational Fluid Dynamics analysis
Salama, A,
J Kou,
S Sun, and
M Hefny,
Transport in Porous Media , 2024. https://doi.org/10.1007/s11242-024-02114-8 [Download] [View Abstract]Understanding the dynamics of the filling process of a pore body with a nonwetting fluid is important in the context of dynamic pore network models (PNM) and others. It can justify many of the assumptions behind the different rules that describe how the network behaves during imbibition and drainage processes. It also provides insight into the different regimes pertinent to this system. The filling process starts with the contact line pinning at the pore entrance. Three regimes can be identified during the filling process that is related to how the contact line advances. In the first two regimes, the contact line pins at the pore entrance while the emerging droplet develops, and in the third one, the contact line departs the entrance of the pore and advances along the pore surface. During the first regime, which is brief, the curvature of the meniscus increases, and likewise, the corresponding capillary pressure, while in the other two regimes, the curvature decreases and so does the capillary pressure. Such behavior results in the rate at which the nonwetting fluid invades the pore to change. It initially decreases, then increases as the meniscus advances. The radius of curvature of the meniscus, eventually, increases to infinity for which the interface assumes a flat configuration. A one-dimensional modeling approach is developed that accounts for all these regimes. The model also considers the two immiscible fluids over a wide spectrum of contrast in viscosity. Information about the mean velocity of the invading fluid, the location of the contact line, the radius of curvature of the meniscus, the volume of the emerging droplet, and several others are among the details that the model provides. A computational fluid dynamics (CFD) simulations have also been considered to confirm the proposed fates of the interface and to provide a framework for comparisons. The results of the validation process show, generally, a very good match between the model and the CFD analysis. (Paper accepted 2024-07-25)
Advancing Fractured Geothermal System Modelling with Artificial Neural Network and Bidirectional Gated Recurrent Unit
Li, Y.,
G. Peng,
T. Du,
L. Jiang, and
X.-Z. Kong,
Applied Energy, 372, 2024. https://doi.org/10.1016/j.apenergy.2024.123826 [Download] [View Abstract]Geothermal energy plays a pivotal role in the global energy transition towards carbon-neutrality, providing a sustainable, renewable, and abundant source of clean energy in the fight against climate change. Despite advancements, the optimal engineering of geothermal systems and energy extraction remains challenging, particularly in accurately predicting production temperatures. Here, we present an innovative numerical approach using a hybrid neural network that merges Artificial Neural Network (ANN) and Bidirectional Gated Recurrent Unit (BiGRU). With this hybrid network, we comprehensively assess 22 influential factors, including construction parameters, physical parameters, and well layout, which influence thermal breakthrough time and production temperature across varying fracture density. While the ANN captures the nonlinear interplay between static constraints and thermal breakthrough time, the BiGRU adeptly handles the temporal intricacies of production temperature. We examine the impact of ANN parameters on model performance, in comparison with conventional temporal models like Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gate Recurrent Unit (GRU), and BiGRU. Our findings reveal that augmenting hidden layers and neurons in ANN enhances its capacity to model intricate nonlinear processes, albeit with a risk of overfitting. Notably, the relu activation function emerges as optimal for managing nonlinear processes, while BiGRU excels over RNN, GRU, and LSTM models in forecasting production temperature of fractured geothermal systems, owing to its ability to extract implicit information from time series across historical and future trajectories. Crucially, the prediction uncertainty, measured by Root Mean Square Error (RMSE) and Mean Absolute Error (MAE), remains within 0.15, underscoring the precision and efficacy of our hybrid approach in forecasting geothermal energy extraction. This study presents a significant stride towards a high-precision and efficient predictive framework crucial for advancing geothermal energy extraction in the broader context of renewable energy transition endeavors. (Paper accepted 2024-06-29)