Mission Statement

The Geothermal Energy & Geofluids group is endowed by the Werner Siemens Foundation and investigates reactive fluid (water, CO2, CxHy, N2) and (geothermal) energy (heat, pressure) transfer in the Earth’s crust employing computer simulations, laboratory experiments and field analyses to gain fundamental insights and to address a wide range of societal goals and concerns. ➞ Read More

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GEG News

A CO2-Plume Geothermal (CPG) Process/Mechanical Engineering/Postdoc position is becoming available on 1st Nov. 2023, or later, in the GEG Group. For more information and to apply, please click here.


Doctoral Examination Batmagnai Erdenechimeg

On March 3, 2023, Batmagnai Erdenechimeg has successfully defended his PhD thesis, entitled: “Magnetotelluric exploration of intermediate temperature geothermal systems and mineral resources in central Mongolia”.


Dr. Paromita Deb – Heitfeld Award

December 2022, RWTH Aachen
Dr. Paromita Deb has been given the Dr. Karl-Heinrich Heitfeld Award for the best doctoral thesis in the Division of Earth Sciences and Geography.

Read more – link to RWTH Aachen


Doctoral Examination Isamu Naets

On October 28, 2022, Isamu Naets has successfully defended his PhD thesis, entitled: “A pore scale investigation of fluid flow heterogeneity and solute transport in rough-walled fractures”.


Ending EGC2022 with great results and a new destination for EGC2025 in Zurich

The biggest geothermal event in Europe has ended. More than 1300 participants attended EGC2022. The next EGC2025 will be held in Zurich, from 6 to 10 October 2025, organized by Geothermie Schweiz and co-organized by ETH Zurich.


Doctoral Examination Po-Wei Huang

On September 16th, 2022, Po-Wei Huang has successfully defended his PhD thesis, entitled: “Reactive transport modeling at the pore scale and upscaling to the Darcy scale”.


CO2-Plume Geothermal (CPG) power plants combine geologic CO2 storage with geothermal energy extraction.
© Shannon Gilley
Inexhaustible resource of clean, renewable Geothermal Energy.
© ETH Zurich

By 2050, geothermal energy can cover 25% of Switzerland’s heating needs in a CO2-neutral way. © Daniel Stegmann

Grimsel rock laboratory, safer drilling methods.
© 3sat nano

GEG Events

26.09.2023    13:15-14:15
Stochastic reservoir modeling in fractured carbonates - Case Study of Geneva Basin

Paromita Deb (GEG group presentation)
GEG Meetings, ETH Zurich

03.10.2023    13:15-14:15
BSc Thesis Pockmarks in Lake Zurich; testing an underwater drone

Aregger Alexandra (GEG group presentation)
GEG Meetings, ETH Zurich

10.10.2023    13:15-14:15
Update on Aquistore study

Kevin Hau (GEG group presentation)
GEG Meetings, ETH Zurich

24.10.2023    13:15-14:15
Updates about PPGD project

Mohamed Ezzat Mostafa (GEG group presentation)
GEG Meetings, ETH Zurich

31.10.2023    13:15-14:15
CPG Consortium update

Jasper de Reus (GEG group presentation)
GEG Meetings, ETH Zurich

05.12.2023    13:15-14:15
Steady-state THM simulation: study case (high-enthalpy geothermal field)

Lily Suherlina (GEG group presentation)
GEG Meetings, ETH Zurich

12.12.2023    13:15-14:15
Coupled reservoir/wellbore simulations of multi-phase multi-component systems

Serhat Kucuk (GEG group presentation)
GEG Meetings, ETH Zurich

Newest GEG Papers

Refereed journal papers accepted the last 6 months

Underlined names are links to current or past GEG members

Mechanisms causing injectivity decline and enhancement in geothermal projects
Luo, W., A. Kottsova, P. J. Vardon, A.C. Dieudonne, and M. Brehme, Renewable and Sustainable Energy Reviews, 185, 2023. https://doi.org/10.1016/j.rser.2023.113623 [Download] [View Abstract]In geothermal projects, reinjection of produced water has been widely applied for disposing wastewater, supplying heat exchange media and maintaining reservoir pressure. Accordingly, it is a key process for environmental and well performance assessment, which partly controls the success of projects. However, the injectivity, a measure of how easily fluids can be reinjected into reservoirs, is influenced by various processes throughout installation and operation. Both injectivity decline and enhancement have been reported during reinjection operations, while most current studies tend to only focus on one aspect. This review aims to provide a comprehensive discussion on how the injectivity can be influenced during reinjection, both positively and negatively. This includes a detailed overview of the different clogging mechanisms, in which decreasing reservoir temperature plays a major role, leading to injectivity decline. Strategies to avoid and recover from injectivity reduction are also introduced. Followed is an overview of mechanisms underlying injectivity enhancement during reinjection, wherein re-opening/shearing of pre-existing fractures and thermal cracking have been identified as the main contributors. In practice, nevertheless, mixedmechanism processes play a key role during reinjection. Finally, this review provides an outlook on future research directions that can enhance the understanding of injectivity-related issues. (Paper accepted 2023-08-02)
Validating the Nernst–Planck transport model under reaction-driven flow conditions using RetroPy v1.0
Huang, P.-W., B. Flemisch, C.-Z. Qin, M.O. Saar, and A. Ebigbo, Geoscientific Model Development, 16, pp. 4767-4791, 2023. https://doi.org/10.5194/gmd-16-4767-2023 [Download] [View Abstract]Reactive transport processes in natural environments often involve many ionic species. The diffusivities of ionic species vary. Since assigning different diffusivities in the advection-diffusion equation leads to charge imbalance, a single diffusivity is usually used for all species. In this work, we apply the Nernst–Planck equation, which resolves unequal diffusivities of the species in an electroneutral manner, to model reactive transport. To demonstrate the advantages of the Nernst–Planck model, we compare the simulation results of transport under reaction-driven flow conditions using the Nernst–Planck model with those of the commonly used single-diffusivity model. All simulations are also compared to well-defined experiments on the scale of centimeters. Our results show that the Nernst–Planck model is valid and particularly relevant for modeling reactive transport processes with an intricate interplay among diffusion, reaction, electromigration, and density-driven convection. (Paper accepted 2023-07-10)
Insights on the interplay of rifting, transcrustal magmatism and formation of geothermal resources in the central segment of the Ethiopian Rift revealed by 3-D magnetotelluric imaging
Dambly, M.L.T., F. Samrock, A. Grayver, and M.O. Saar, Journal of Geophysical Research: Solid Earth, 128, 2023. https://doi.org/10.1029/2022JB025849 [Download] [View Abstract]The Main Ethiopian Rift is accompanied by extensive volcanism and the formation of geothermal systems, both having a direct impact on the lives of millions of inhabitants. Although previous studies in the region found evidence that asthenospheric upwelling and associated decompression melting provide melt to magmatic systems that feed the tectono-magmatic segments in the rift valley, there is a lack of geophysical models imaging these regional and local scale transcrustal structures. To address this challenge, we use the magnetotelluric method and image subsurface electrical conductivity to examine the magmatic roots of Aluto volcano, quantify and interpret the melt distribution in the crust considering established concepts of continental rifting processes and constrain the formed geotherma system. Specifically, we combined regional (maximum 30 × 120 km2) and local (15 × 15 km2) magnetotelluric data sets and obtained the first multi-scale 3-D electrical conductivity model of a segment of the central Main Ethiopian Rift. The model unravels a magma ponding zone with up to 7 vol. % melt at the base of the crust (30 − 35 km b.s.l.) in the western part of the rift and its connection to Aluto volcano via a fault-aligned transcrustal magma system. Melt accumulates at shallow crustal depths (≥ 4 km b.s.l.), thereby providing heat for Aluto’s geothermal system. Our model suggests that different volcano-tectonic lineaments in the rift valley share a common melt source. The presented model provides new constraints on the melt distribution below a segment of the rift which is important for future geothermal developments and volcanic hazard assessments in the region. (Paper accepted 2023-06-29)
Geophysically guided well siting at the Aluto-Langano geothermal reservoir
Samrock, F., A. Grayver, M.L.T. Dambly, M.R. Müller, and M.O. Saar, Geophysics, 88, pp. 1-43, 2023. https://doi.org/10.1190/geo2022-0617.1 [Download] [View Abstract]Volcano-hosted high-temperature geothermal reservoirs are powerful resources for green electricity generation. In regions where such resources are available, geothermal energy often provides a large share of a country’s total power generation capacity. Sustainable geothermal energy utilization depends on the successful siting of geothermal wells, which in turn depends on prior geophysical subsurface imaging and reservoir characterization. Electromagnetic resistivity imaging methods have proven to be a key tool for characterizing magma-driven geothermal systems because resistivity is sensitive to the presence of melt and clays that form through hydrothermal alteration. Special emphasis is often given to the “clay cap,” which forms on top of hydrothermal reservoirs along the flow paths of convecting geothermal fluids. As an example, the Aluto-Langano volcanic geothermal field in Ethiopia was covered with 178 densely spaced magnetotelluric (MT) stations. The 3D electrical conductivity model derived from the MT data images the magma body that acts as a heat source of the geothermal system, controlling geothermal convection and formation of alteration zones (commonly referred to as clay cap) atop the geothermal reservoir. Detailed 3D imaging of the clay cap topography can provide direct insight into hydrothermal flow patterns and help identify potential “upflow” zones. At Aluto all productive geothermal wells were drilled into zones of clay cap thinning and updoming, which is indicative of underlying hydrothermal upflow zones. In contrast, nonproductive wells were drilled into zones of clay cap thickening and lowering, which is an indicator for underlying “outflow” zones and cooling. This observation is linked to fundamental characteristics of volcano-hosted systems and can likely be adapted to other geothermal fields where sufficiently detailed MT surveys are available. Therefore, high-resolution 3D electromagnetic imaging of hydrothermal alteration products (clay caps) can be used to infer the hydrothermal flow patterns in geothermal reservoirs and contribute to derisking geothermal drilling projects. (Paper accepted 2023-06-02)
Deep learning based closed-loop well control optimization of geothermal reservoir with uncertain permeability
Wang, N., H. Chang, X.-Z. Kong, and D. Zhang, Renewable Energy, 211, pp. 379-394, 2023. https://doi.org/10.1016/j.renene.2023.04.088 [Download] [View Abstract]To maximize the economic benefits of geothermal energy production, it is essential to optimize geothermal reservoir management strategies, in which geologic uncertainty should be considered. In this work, we propose a closed-loop optimization framework, based on deep learning surrogates, for the well control optimization of geothermal reservoirs. In this framework, we construct a hybrid convolution–recurrent neural network surrogate, which combines the convolution neural network (CNN) and long short-term memory (LSTM) recurrent network. The convolution structure can extract spatial information of reservoir property fields and the recurrent structure can approximate sequence-to-sequence mapping. The trained model can predict time-varying production responses (rate, temperature, etc.) for cases with different permeability fields and well control sequences. In this closed-loop optimization framework, production optimization, based on the differential evolution (DE) algorithm, and data assimilation, based on the iterative ensemble smoother (IES), are performed alternately to achieve a real-time well control optimization and to estimate reservoir properties (e.g. permeability) as the production proceeds. In addition, the averaged objective function over the ensemble of geologic parameter estimates is adopted to consider geologic uncertainty in the optimization process. Geothermal reservoir production cases are examined to evaluate the performance of the proposed closed-loop optimization framework. Our results show that the proposed framework can achieve efficient and effective real-time optimization and data assimilation in the geothermal reservoir production process. (Paper accepted 2023-04-18)