The magnetotelluric (MT) method is a well-established tool in geothermal exploration. Case studies from all over the world and from different geothermal settings have proven its effectiveness, when it comes to subsurface reservoir characterization and the successful siting of geothermal wells. A reason for MT being a popular tool in geothermal exploration is that the bulk electrical conductivity of the subsurface, as recovered by MT, can be used as a proxy for key geothermal parameters. For example, fluid saturation and connectedness, hydrothermal alteration and active magmatic heat sources all significantly influence electrical conductivity and appear as electrically conducting zones in the subsurface. In the field, MT surveying benefits from little manpower requirements, low environmental impact and from the fact that natural electromagnetic source signals are permanently present everywhere on the globe. Whilst MT is successfully used in sparsely populated regions, challenges arise when it comes to MT exploration in populated areas. Here, data acquisition is prone to noise issues that arise from local infrastructure that make MT often too cumbersome for commercial applications. The interpretation of MT data is improving continuously with the development of powerful numerical modelling tools. 3-D subsurface models with flexible meshes that adapt to topography and varying data resolution allow one to characterize geothermal systems from their surface manifestations down to their deeper roots in the lower crust.

We present case studies from three high-temperature geothermal systems in the East African Rift, where MT is successfully used to image magmatic reservoirs that drive convection of hydrothermal fluids. As demonstrated, recovered MT models of these systems play a key role when it comes to the successful siting of geothermal wells, which are commonly drilled into permeable up-flow zones above shallow magmatic reservoirs. In another case study from an intermediate-temperature geothermal system in the Mongolian Hangai, we demonstrate how large datasets with more than 300 MT stations can be acquired by small academic teams. However, the interpretation of MT subsurface models from such intermediate-temperature systems in non-volcanic terrains is less straight forward and requires more a priori knowledge and interdisciplinary strategies as compared to MT studies of high-temperature volcanic geothermal systems.

The Upper Rhine Graben (URG) is a target area for deep geothermal and heat storage projects, as petrophysical and hydraulic properties of the faulted crystalline basement rocks, and the temperature field comprise a high geothermal potential (Soultz-sous-Forêts, Landau, Insheim, Rittershoffen). However, there is a lack of knowledge on the multi-scale structural architecture of such rock units in fault zones. Therefore, a multi-scale structural analysis is performed on surface analogues to improve the conceptual crystalline reservoir model accuracy. The surface analogues selected are located in the Odenwald Massif, the largest outcropping section of the Mid German Crystalline High. Regional-scale lineament analysis and LIDAR and GIS interpretation of the fracture network on 21 profiles in 11 outcrops were analysed to quantify statistical parameters describing the fracture and fault network. The variability of crystalline rock lithologies (granite, granodiorite, ‘Flasergranitoid’, amphibolite and gabbro) and fault directions sampled allows for the construction of an extensive structural dataset with fracture network geometry, dimension, and connectivity features. Four significant lineament strikes dominate the structural trend of the NURG, being N000-N015°E, N050-N075°E, N100-N115°E and N150-N165°E. In the Odenwald itself, lineaments striking N100-N115°E and N055-N070°E are in a predominant proportion, compared to the N000-N015°E and N150-N165°E striking trends. Fracture length distribution follows a power law with an exponent varying from -2.2 to -1.8, depending on the background lithology. The connectivity of the fracture network is heterogeneous, with varying configurations (no fractal organisation), due to a fault control at hectometric scale and clustering marked by secondary faults. At the outcrop scale, this pattern is strongly enhanced in the vicinity of weathered fractures or fault corridors. These properties distribution can be implemented into sub-surface semi-artificial discrete fracture network models to quantify the flow properties of fractured reservoir rocks.

In the northern Upper Rhine Graben (URG), the crystalline basement constitutes an attractive target for deep geothermal exploitation due to the favourable reservoir temperatures and abundance of natural fractures and large-scale faults. Consequently, especially the upper, hydrothermally altered part is already successfully used for heat and power generation at several locations (Insheim, Landau, Rittershoffen and Soultz-sous-Forêts). Nevertheless, because of the small number of very deep boreholes drilled into the crystalline basement, little is known about its structure and composition. An interdisciplinary multi-scale approach was applied to gain new insights into the properties of the crystalline crust. By building on existing geological models of the URG, a detailed 3D model of the crystalline basement was developed. Additional information was provided by high-resolution gravity and magnetic data, which served as input for a stochastic joint inversion. Inverted density and susceptibility models allowed to identify the predominant rock types below the sedimentary cover. The Tromm granite in the southern Odenwald was chosen as an outcrop analogue to further analyse the hydraulic properties of the crystalline reservoir. By examining the lineaments on the regional scale and the fractures in a total of 5 outcrops, statistical parameters describing the fracture network were extracted. These were then used to create discrete fracture network (DFN) models, in order to calculate the equivalent porous media permeabilities of the bedrock at reservoir depth. In addition, gravity and radon measurements were carried out, which enabled more precise localisation of naturally permeable fault zones. The combination of structural geological and geophysical methods results in a more advanced characterisation of the crystalline basement, that can in future studies be used for more realistic potential assessments and a reduction of exploration risks for geothermal projects.

Solutions for seasonal energy storage systems are an essential component for the reliable use of fluctuating renewable energy sources and to bridge the gap between abundant heat availability from renewable sources in summer and an increased heat demand in winter. As a part of the research project ’solar crystalline borehole thermal energy storage system‘ – ‘SKEWS’, a field-scale demonstrator for a medium deep borehole thermal energy storage (BTES) system with a maximum depth of 750 m is to be built at Campus Lichtwiese of the Technische Universität Darmstadt, Germany, to demonstrate this innovative technology. In this first demonstration phase, the storage array consists of four coaxial BHEs (Borehole Heat Exchangers), which tap into a crystalline reservoir rock underneath a thin sedimentary cover. Prior to project launch a numerical model of the storage system was to be built to investigate the storages behavior under the local geological and hydrogeological conditions. In the first stage, the geological context in the surroundings of the project location was investigated using archive drilling data and groundwater measurements. The data obtained facilitated the development of a geological model concept. It suggests the assumption that the uppermost part of the intended storage domain is crosscut by a normal fault, which displaces the Permian rocks east of Darmstadt against granodioritic rocks of the Odenwald crystalline complex. The simplified geological model was implemented in a 3D-finite-element numerical model to simulate the thermal effect of the storage system operation on the surrounding subsurface. The model for the four planned BHEs did not show the formation of any significant heat plumes by groundwater flow with only a minor increase in groundwater temperature.

Additionally, the numerical model was used to estimate the effect of the potentially highly permeable fault zone on the planned storage site. For this purpose, a storage operation over a time span of 30 years was simulated for different parametrizations of the fault zone and the storage system. The simulations reveal a limited but visible removal of heat from the storage region with increasing groundwater flow in the fault zone. However, since the section of the BTES system affected by the fault is very small in comparison to the system’s total depth, only a minor impairment of the storage efficiency could be observed in the worst case.

The seasonal mismatch of the thermal energy demand can be addressed by thermal energy storage systems of high capacity (e.g. Lee 2013). In this scenario, High-temperatures aquifer thermal energy storage systems (HT-ATES), which commonly supply domestic needs could expand to meet heating or industrial processes demands by storing excess heat.

DeepStor is a planned scientific infrastructure that address the demonstration the concept of HT-ATES in former hydrocarbon reservoirs of deep sedimentary rocks. Specifically, the concept development of the use of deep geothermal energy at Campus North (CN) of KIT. The latter is located on the largest known thermal anomaly in Germany (up to 140°C at 2 km depth), and in the central part of the Upper Rhine Graben (URG).

In order to improve the understanding of the depleted oil reservoir conditions, a gravity survey is being carried out to support the modeling of geological structures. The gravity data in and around the CN is being acquired in an area of ~10 km2 using a CG-6 Autograv Gravity Meter (Scintrex Ltd) has a measurement range of over 8000 mGals and a resolution of 0.0001 mGal. This enable to study in both detailed local and large scale regional structures.

Previous works in the URG have shown that temperatures above 100°C located in the central part of the graben superpose with areas of low values of Bouguer anomaly (Baillieux et al., 2013). On the other hand, the gravity observations on the URG have been interpreted in terms of subsurface density variation due to lithological heterogeneities.

The results of the new gravity data at a local scale will improve the understanding of the local lithological heterogeneities and fracture porosity, giving feedback for the improvement of the new geological model in this area.

A transient 3D model obtained from the Piesberg quarry as an illustrative example is based on idealized structural models that characterize all geological features during Late Jurassic rifting (162 Ma) to infer possible transport mechanisms of fluids leading to the formation of kilometer-scale thermal anomaly. Three-dimensional numerical simulations on hydrothermal convection systems in the faulted sedimentary basins are investigated with the aim to assess the lateral heating capacity of hydrothermal convection systems in faults, using realistic rock properties (widths, inclinations, anisotropic permeabilities, etc.), fault dimensions, and fault intersections patterns and using variable parameter suites to assess the effect of lateral heating derived from hydrothermal convection systems. Three kinds of transport mechanisms of hydrothermal convection have been inferred and the effects of geological conditions on the transport mechanisms of hydrothermal convection in faulted sandstone reservoirs have been discovered. Furthermore, this study demonstrates that the local thermal anomalies are presumably provoked by circulating hydrothermal fluids along the fault damage zone of a large NNW-SSE striking fault, laterally heating up the entire exposed sandstone reservoir. Results suggest that this thermal event was reached prior to peak subsidence during Late Jurassic rifting (162 Ma). Owing to the idealized nature of the presented models, the numerical results and the associated analytical solution can be applied to petroleum and geothermal system models to avoid overestimating burial depth and reservoir quality, etc.

The application of geothermometry has been used for the last six decades for geothermal reservoir temperature estimation. A steady evolution of conventional geothermometers to multicomponent tools as well as application of artificial intelligence are nowadays available.
The development of high-performing computers offers the possibility to use deep learning algorithm for reservoir temperature estimation. Serving a selection of geochemical input parameters to artificial neural networks, they can be used to predict temperatures in the subsurface. Therefore, the chemical composition of the geothermal fluids are required. Main cations and anions as well as the SiO₂ concentration and the pH value serve as these input parameters. Using the data of well-studied geothermal systems, the neurons within the layers of the neural network are linked and weighted. Thus, the newly developed artificial intelligence is trained and validated. As a result, the modelled reservoir temperatures match with the in-situ temperature measurements of the analysed geothermal fields. Contrary to the usage of conventional geothermometers, the application of artificial neural networks are a useful novelty. While dealing with large amounts of data, artificial neural networks are faster, more easy-to-handle, as well as higher in accuracy.

In the discussion about the future role of geothermal in the energy transition policy, the topic of underground heat storage became recently more and more prominent. High Temperature Aquifer Storage (HTAS) may make geothermal more efficient by extending it beyond its traditional usage as a base load with also covering middle and even peak load. Depleted oil reservoirs can provide this underground storage capacity and Stricker et. al (2021) have numerically described the thermal storage potential in depleted oil fields from examples of the Upper Rhine Graben.

Hydrocarbon exploration and production in the Northern Alpine Foreland Basin accelerated after 1950. It reached its peak in the 1980s, and then decreased mainly due to the low oil prices. Numerous separated reservoir units were successfully developed and exploited. The related extensive exploration campaigns provide exhaustive seismic profiles and borehole data for delineation of geometric underground features and reservoir properties. Since the outgoing 1990, parts of this data were already applied for the successful hydro-geothermal exploration of the Upper Jurassic Malm, especially in the greater Area of Munich and the Eastern part of the Molasse.

The present study focusses on the geological and hydrogeological potential of high temperature storage in the surrounding of the existing oil fields in the South German Molasse basin. Reservoir information and data as e.g. thickness, porosity and depth of the reservoir rock as well as overlying barrier properties are compiled from two meta-studies, the Geothermal Atlas of Bavaria (STMWi, 2004) and Storage Catalogue of Germany (BGR, 2011).

As a result, about one third of the area of the Bavarian Molasse shows a potential underground storage with a reservoir thickness of 10 m and more in depths between about 500 and 1700 m. In the Western part, the potential storage units are the “Bausteinschichten” of the Lower Oligocene with a porosity ranging from 5 – 31 %, and the Middle Jurassic Dogger “Eisensandstein” with an average of 15%. In the Eastern part, Chattian sandstones of the Upper Oligocene with porosities of 20% are present. In a next step, oil field information with the borehole data and its exploitation history has to be investigated, to gather more details on local reservoir characteristics as e.g. temperature, pore pressure and to develop an exploration and exploitation strategy to better determine the uncertainties and risks.

The inaccessibility of geothermal reservoirs makes the accurate determination and monitoring of reservoir properties and conditions difficult and is a major problem in reservoir engineering. We present an approach for the development of messenger nanoparticle tracers for the simultaneous determination of flow paths ("tracer") and reservoir properties ("messenger"), with a proof-of-concept example of flow-through experiments and temperature detection under controlled laboratory conditions. For this, silica particles are synthesized with a two-layer architecture, an inner closed core and an outer porous shell, each doped with a different fluorescent dye to create a dual emission system. Temperature detection is achieved by a threshold temperature-triggered irreversible release of the outer dye, which changes the fluorescence signal of the particles. The flow-through experiments were conducted in a sand packed-bed column. The breakthrough curves of the nanoparticle tracers show minor tailing and a faster breakthrough compared to conservative, conventional molecular tracers such as Uranine and Eosine. The presented particle system thus provides a direct, reliable and fast way to determine reservoir temperature and flow paths in the reservoir. The system has a sharp threshold for accurate measurement and allows detection in concentration ranges as low as a few micrograms of nanoparticles per liter.

Between 1993 and 2005, the Soultz-sous-Forˆets reservoir was stimulated through 4 different wells crossing the reservoir at two different levels R3 (about 3km deep) and R5 (about 5km deep). The figure below represents the N-S section of the reservoir with the geometry of the 4 wells. During these stimulation episodes, seismic and hydraulic data were recorded. Using hydraulic data (pressure and flow rate) and available seismic catalogs of the stimulation episodes in the Soultz-sous-Forˆets reservoir, an analysis of the evolution of the injected energy and seismic energy was made. The analysis revealed two seismic behaviors of the reservoir. First, the seismic energy grows linearly with the energy injected from a certain level of energy injected with a similar slope for wells stimulated a first time. The parts of the reservoir which are stimulated a second time (GPK1 in 1993 and 1996 and GPK4 in 2004 and 2005) show a more rapid growth of the seismic energy which can be explained by the Kaiser effect (a reservoir stimulated a first time will have to reach at least the maximum pressure level reached during the first stimulation to generate seismic activity again). Secondly, the seismic response of the deepest part of the reservoir (R5) is greater than the shallowest one (R3). Indeed, the injection efficiency, which is calculated by the ratio between the cumulated seismic energy and the cumulated injected energy shows a convergence towards 10−5 for R3 and 10−2 for R5.

The swelling of clay-sulfate rocks is a well-known phenomenon often causing threats to the success of different projects, for instance, geothermal drillings triggered swelling and ground heave with dramatic damages in Staufen, Germany. The origin of clay-sulfate swelling is usually explained by physical swelling due to clay expansion combined with chemical swelling associated with the transformation of anhydrite (CaSO₄) into gypsum (CaSO₄.2H₂O). The swelling leads to about 60% of the volume increase of the rock mass. Numerical models simulating rock swelling must consider hydraulic, mechanical, and chemical processes. The simulation of the chemical processes is performed by solving thermodynamic equations usually contributing a significant portion of the overall simulation time. This contribution presents a Gaussian process regression (GPR) model as an alternative approach to determine the solubility of mineral phases, i.e., anhydrite and gypsum, in pore water. The GPR model is developed using the experimental data collected from the literature. The GPR predicts the solubility of the sulfate minerals with a degree of accuracy needed for typical subsurface engineering applications.

Scientific research is carried out in the framework of the INSIDE project (supported by the German Federal Ministry for Economic Affairs and Energy, BMWi) to assess the impact of deep geothermal exploitation on induced seismicity in the Munich area (Germany, Molasse Basin). The project involves the research institute Karlsruhe Institute of Technology as well as two geothermal operators, Stadtwerke München (SWM) and Innovative Energie für Pullach (IEP). The research work focuses on three aspects: the monitoring, the modelling and the integration with operations.

With respect to the monitoring, the deployment of a measurement network going beyond the standard for seismological and geodetic observations is considered. Therefore, an extensive and plural monitoring network was designed to monitor high (seismicity) and low (subsidence, uplift) frequency deformation processes of the subsurface. Several types of technologies as well as several types of deployment configurations are involved. Their relative performances are intended to be compared in order to contribute to the development of suitable strategies for deformation monitoring and their data processing.

After presenting the aim and purpose of the project, we concentrate on the status of the seismic measurement network being implemented around the three geothermal sites of Baierbrunn, Pullach and Schäftlarnstrasse. In addition to “standard” monitoring stations installed in the area, we report on the deployment of various innovative technical solutions, among which a seismic mini-array and a monitoring borehole dedicated to Distributed Acoustic Sensing (DAS). We show how these stations complement the existing network in Munich and present their main characteristics, in particular the associated noise measurements. We additionally discuss the data-management system being developed to handle all these new data.

Geothermal energy represents around 30% of the produced electricity in Iceland with a cumulative capacity being equal to 755 MWe (Ragnarsson et al., 2020). In particular, the Theistareykir geothermal plant, which is located on the Mid-Atlantic ridge in North Iceland, produces 90 MWe using two turbines in operation since autumn 2017 and spring 2018, respectively. We will report on the hybrid micro-gravity monitoring and discuss how this technique will contribute to the sustainable management of this renewable energy. Indeed, the gravity method highlights the mass redistribution and, consequently, helps to quantify the recharge/discharge of the geothermal reservoir.

On one hand we show the results of the repetition of the Theistareykir micro-gravity network of 27 stations measured in summer 2017, 2018 and 2019 i.e. before and after the beginning of the geothermal production, with a Scintrex CG5 gravimeter.

On the other hand, we will also show the continuous gravity changes recorded from fall 2017 to summer 2020 at 3 permanent stations with iGrav superconducting gravimeters calibrated with a FG5 ballistic absolute gravimeter.

The combination of these different types of gravimeters defining the hybrid micro-gravity method is then used to investigate the measured gravity changes in relation to geothermal activity parameters like injection and extraction rates.

After correcting the gravity measurements for the effect of the vertical displacements deduced by continuous GNSS measurements at the permanent stations and InSAR analysis by the University of Iceland, we compare the gravity changes due to mass redistribution to what is expected from the injection/extraction rates.

We finally focus on the question of the sustainability of the Theistareykir power plant since the start of exploitation and discuss the discharge/recharge of the geothermal reservoir.

Ragnarsson, Á., Steingrímsson, B. and Thorhallsson, S. Geothermal development in Iceland 2015-2019. Proceedings World Geothermal Congress 2020, Reykjavik, Iceland (2020).

Geothermal district heating networks are among the key options to decarbonize the heating sector in the State of Geneva in Switzerland. But the development of geothermal district heating requires high capital costs and involves risk of not finding sufficient geothermal resources, which make these systems less competitive. On the other hand, building geothermal district heating creates a wider impact on the economy, domestically and overall. But such impact has rarely been evaluated.

Our study aims to analyze the competitiveness of geothermal district heating networks and their wider economic impacts using two competitiveness indicators (levelized costs of geothermal district heating and of district heating system as a whole) and two economic impact indicators (economic impact multipliers and share of domestic economic impacts in Switzerland). We construct a decision tree to generate 9’096 decision paths to develop shallow and medium geothermal district heating in the State of Geneva comprising 10 decision parameters: target of heat demand to be supplied (100 GWh/year and 400 GWh/year), number of districts (1,2,3 and 4 districts), share of geothermal coverage in the district heating system (10%, 40%, 70% and 100%), choice of auxiliary heating source, district heating generation (second, third and fourth), linear heat density (2, 4, 6, and 8 MWH/m•year), geothermal well depths (800 m, 1600 m, 2500 m), geothermal flowrates (20 l/s, 50 l/s, 80 l/s), and 3D seismic exploration program (with or without). We quantify the four indicators for each decision path in a decision tree, including applying probability trees to account for geothermal resource risk through assigning probabilities of success. We then identify the most influential decision parameters using a random forest regression and pinpoint the decision paths that lead to low levelized costs of heat and high economic impact multipliers and share of domestic economic impact. Finally, to analyze the synergies among the four indicators, we identify the common key decision parameters and the decision paths leading to synergies between having low levelized costs and high economic impacts.

The results demonstrate significant variation in the values of four indicators of levelized costs and economic impact, depending on the combination of the 10 aforementioned decision parameters. The influence of geothermal coverage is observed in all four indicators, although more strongly in the variation of levelized cost indicators. For the variation of the economic impact indicators, the choice of auxiliary heating source has a stronger influence than geothermal coverage. We identify that synergy could be achieved in scenarios having 40% geothermal heat and 60% heat from centralized waste incineration, deployed together in a district with a linear heat density of 6 or 8 MWh/(m.year), using second district heating generation.

Our study shows the importance of integrating a combination of many decision parameters to understand the competitiveness and economic impacts of geothermal district heating. Focusing on geothermal coverage, linear heat density, district heating generation, and choice of auxiliary heating sources makes the biggest difference when setting up economically meaningful strategies.

The modification of the stress field induced by fluid injection into the ground can generate seismic motions. Their monitoring is a key point to limit the occurrence of impacting events. Generally, this is performed using seismic surface networks, which can be limited by a significant ambient noise level especially in urban contexts. An alternative consists in the installation of stations in the depth of wells to increase the distance with surface ambient noise sources. However, few are the industrial projects fitted with such technologies, because of their cost and complexity of installation. Another possibility is to operate with dense seismic networks (seismic arrays), combined with appropriate data processing, to limit the impact of anthropogenic noise by distinguishing it from earthquakes. Here we investigate the case of the “Strasbourg induced earthquake sequence”, occurring since mid-2018 around the Geoven deep geothermal doublet operated by the Fonroche company in Vendenheim (France). So far, the BCSF-Rénass (national observatory service in charge of the french seismicity monitoring) has recorded 567 induced earthquakes using traditional local and regional seismic networks. Their catalogue has an estimated magnitude of completeness of Mc=0.6 at best, containing event with a local magnitude (Mlv) up to 3.9, including 22 with Mlv>2 and 4 with Mlv>3. These events are organized into two distinct swarms: a first cluster in the vicinity of the Geoven wells and a second one 4-5km South from it. Although the project has been forced to stop because of the felt induced seismicity, the Northern cluster is still very active, with the largest event occurring the 26^th of June 2021. To improve our knowledge of this seismic crisis, we deployed 3 mini seismic arrays of 21 SmartSolo nodes each around the active cluster, recording at a sampling rate of 1000Hz for 4 months starting a few days after the Mlv=3.6 event of 4^th of December 2020. The aperture of each array is around 70m, allowing good wave number resolution in the frequency range of interest for local seismic events. Beamforming and match field processing techniques allow us to characterize the local ambient noise, which consists mostly in surface waves with slow apparent velocities. As the arrays are located roughly on the top of induced seismic events hypocenters, the front waves illuminate the arrays with a significantly higher apparent velocity. Therefore, stacking brutally the waveforms increases drastically the SNR. We improve it even more by considering the signal instantaneous phase as a coherency parameter during the stacking process, what is called phase-weighted stacking. This allows us to detect events down to magnitude -0.5, which leaves us with 4 to 5 times more events than the BCSF-Rénass catalogue. In parallel, we also investigate how much these arrays can improve event location as a complement to traditional networks.

Acid fracturing technique has been widely used in the oil and gas industry for improving the carbonate reservoir permeability. In recent years this chemical stimulation technique is borrowed from the oil and gas industry, employed in the enhanced geothermal systems at Groß Schönebeck, Germany (Zimmermann et al., 2010), and at Soultz-sous-Forêts, France (Portier et. al., 2009). In principle, acid fracturing technique utilizes strong acids that react with acid-soluble rock matrix to non-uniformly etch the fracture surfaces. The permeability-enhancing effect depends upon the degree of surface irregularity after pore-scale acidizing which is affected by the compositional heterogeneity of the reacting rock matirx, fracture aperture heterogeneity, and flow and transport heterogeneity. In order to have an insight into these impacts on the acid etching process with the final goal of determining optimum operating conditions (e.g., acid type and acid injection rate), pore-scale acid-fracturing model is needed. The core components of the pore-scale acid-fracturing model consist in tracking the motion of the fluid-matrix boundary surface induced by acid etching. To date, a number of front tracking approaches (e.g., local remeshing technique, embedded boundary method, immersed boundary method, and level-set method) have been proposed by many researchers in order for moving boundary problems. Each approach has its pros and cons. In this work, we propose employing the phase-field approach as an alternative to the existing front tracking approaches to capture the physically sharp concentration discontinuities across the liquid-solid interface. The developed pore-scale acid-fracturing model includes the Stokes-Brinkmann equations for fluid flow in the fracture-matrix system, the multi-component reactive transport equation for transport of solute species in the rough-walled fracture, and the phase-field equation for the reaction-driven motion of the fluid-matrix boundary surface. The simulation results show that the developed pore-scale acid-fracturing model enables to track recession of carbonate fracture surface by acid etching and to capture the solute concentration jump (w.r.t., Ca²⁺, H⁺, and HCO₃⁻) across the solid-liquid interface.

Reference

Zimmermann, G., Moeck, I. and Blöcher, G., 2010. Cyclic waterfrac stimulation to develop an enhanced geothermal system (EGS) — conceptual design and experimental results. Geothermics, 39(1), pp.59-69.

Portier, S., Vuataz, F.D., Nami, P., Sanjuan, B. and Gérard, A., 2009. Chemical stimulation techniques for geothermal wells: experiments on the three-well EGS system at Soultz-sous-Forêts, France. Geothermics, 38(4), pp.349-359.

The undisturbed or static formation temperature (SFT) is a key objective of the borehole measurements analysis. Conventional methods to estimate SFT require borehole temperature data measured during thermal recovery periods. As such, shut-in conditions should prevail for temperature logging, which can be both economically and technically prohibitive in actual operational conditions, especially for high-temperature boreholes. This study investigates the use of temperature logs obtained under injection conditions for SFT determination by applying a Bayesian inference approach--Markov Chain Monte Carlo (MCMC). In particular, surrogate models are trained using artificial neural networks to replace the original high-fidelity numerical models to save computational effort. The inversion scheme is firstly tested on three different synthetic scenarios where the formation all consists of multiple thermal layers (i.e., the initial geothermal gradient of each layer can be different). The results indicate a significant success of the method in predicting SFT profiles, given that the borehole temperature data and the surrogate model are accurate. In addition, if a fluid loss zone occurs along the borehole, the error of the estimated SFT below the loss zone is likely to increase. Furthermore, errors in the measured data also have a significant impact on the quality of the SFT estimates. For example, if the measurement has an error of ±1°C, the predicted SFT is found to have maximum errors ranging from 16.7 °C to 47.2 °C in the 95% confidence interval. Therefore, high-quality temperature data needs to be used to achieve reliable estimation results, and the uncertainty in the measured data should be integrated into the inversion procedure if possible. Finally, the method was applied to a real-world example where the SFT near the RN-15/IDDP-2 well in Iceland is estimated using drilling temperature data. As mentioned in Friðleifsson et al. 2020, the Reykjanes geothermal system exhibits both conductive and convective heat transport behavior in the formation at different depths. Therefore, this study also investigates different assumptions about the shape of the SFT profile. In one hypothesis, the thermal gradient is constant. In another, the formation consists of multiple layers where the thermal gradients can be different from each other. For each scenario, fluid losses at three reported depths during the drilling are jointly estimated with the SFT. The inversion results show that the predicted fluid losses are almost the same (with differences being less than 0.3%) under the two different hypotheses. However, the estimated SFT values can have much difference (maximum ~80 °C) at depths. Our results will be compared with other studies that use geophysical data to assess the formation temperature around the well. Their implication about the geothermal field around the investigated deep hot well will also be discussed.

Fractures are main flow paths for heat extraction from fractured geothermal systems. The process of injecting cold water to extract hot water from a fractured reservoir results in thermal and poroelastic stresses in the rock matrix. Therefore, these thermo-hydro-mechanical (THM) mechanisms govern the efficiency of an enhanced geothermal system (EGS) operation. Fractures’ aperture is a controlling factor for the heat extraction efficiency. Due to the lack of field and experimental works, a constant aperture is considered for all fractures in previous works. However, insights from outcrop or wellbore shows that there is a possibility of some relationships between fracture length and its aperture. To shed light on the effect of this relationship on the heat extraction efficiency, numerical simulations are conducted on a fully coupled THM manner in which the fracture aperture is controlled by the thermo-poroelastic stress. 100 fractures from a Discrete Fracture Network (DFN) are taken as a basis during simulations. For the sake of the computational efficiency, a two‐dimensional planar model (1000 m × 600 m) is selected. Three types of relationships between fracture length and fracture aperture as constant aperture, linear and power law relationships are considered here. To have a better comparison between different cases, a constant value is used for the summation of the ''fracture length multiplied by fracture aperture'' for these three cases.

Fluid and rock properties are selected from the literature in a way to be a good representative of actual cases. Furthermore, fluid properties dependency on pressure and temperature of the system is implemented through the well-known correlations. Constant pressure is assumed as the boundary condition for the injection and production wells. All fractures within the domain are regarded as internal boundaries, implicitly considering the mass and energy exchange between porous media and fractures. We have constrained the displacement in all normal directions. All boundaries of the modeled domain are no flow for both fluid and heat transmission. The local thermal non-equilibrium theory is adopted to simulate the heat exchange between the rock matrix and the flowing fluid. For rock matrix, the energy transfer process is mainly dominated by the heat conduction and the heat exchange between pore fluid. Simulation results reveals that fracture aperture dependency on fracture length is an important factor for heat extraction efficiency from the fractured geothermal systems and requires future attention to this missing factor in the literature. Considering constant aperture results in the later thermal breakthrough which would affect the techno-economic analysis in comparison to the real field data. Possibility of a linear relationship would eventuate the lowest performance between the examined cases.

Determining fluid flow through natural fractures is an important task in many geoscience-related fields, such as geothermics. In order to estimate crucial parameters of single fractures controlling the flow and flow distribution, for example hydraulic apertures, hydro-mechanical numerical models have been established in recent years in addition to experimental methods. Although models enable a greater variety of analyses, they still require time-consuming processing before and after the simulation.

This study presents a novel workflow for hydro-mechanical modeling of a single fracture, with a particular focus on simplifying and shortening data preparation and calibration. First, a Python code matches laser scans of two fracture surfaces by enabling translation in the x-y-direction, minimizing the average mechanical aperture between the fracture surfaces, and automatically generating an input file for numerical modeling in MOOSE. Hydraulic simulations are conducted representing the fracture as a 2D-domain in a 3D-environment and computing Darcy velocities based on the cubic law. The additional use of an external mechanical contact model enables theoretical deformation of the fracture due to normal stress and thus estimation of flow under different lithostatic pressures representative of depths between 50-5,000 m. Subsequently, a mobile air permeameter is used to obtain calibration data. The entire workflow was tested on a bedding joint in a sandstone block sample (Flechtinger Sandstone, North German Basin).

Initial hydraulic simulations without mechanical stress result in hydraulic apertures between 509 µm and 604 µm depending on the matching type, whereas the measured aperture is 82.2 µm. Consequently, the surfaces are matched by preconditioning of the initial contact area. The best consistency between measured and modelled hydraulic aperture is achieved when the contact area is equivalent to 33.5 % of the fracture surface. In addition, the velocity distribution in the fracture indicates that the flow generally occurs along few preferential pathways that are structurally predetermined by smaller fissures or mineralogically distinct veins characterized by higher mechanical apertures or smoother mineral surfaces. Due to the high proportion of contact area, the flow through the fractures is highly localized. The results of the mechanically deformed fractures illustrate an exponential reduction of the hydraulic apertures with depth. The hydraulic aperture converges at approximately 50 µm which is representative of depths that are significantly larger than 5,000 m.

The change of the pathway distribution and the exponential reduction of hydraulic apertures at increasing contact area seem realistic and are comparable to results of other studies. Although the preconditioned contact area of 33.5 % appears to be very high, initial contact areas of up to 20 % were also found in other studies of the Flechtinger Sandstone. In conclusion, this study displays a less time-consuming workflow compared to conventional methods. In future work, a further adaptation could be achieved by creating the surface scans using the dense image matching (DIM) method, which is more flexible and less cost-expensive as laser scanners.

The CDGP [https://cdgp.u-strasbg.fr], Data Center for Deep Geothermal Energy, was created in 2016 by the LabEx G-Eau-Thermie Profonde (continuing now in ITI GeoT) [https://geot.unistra.fr/], to archive the high-quality data collected in the Upper Rhine Graben geothermal sites and to distribute them to the scientific community for R&D activities, taking Intellectual Property Rights into account. It manages seismological (catalogues, waveforms, focal mechanisms), seismic, hydraulic, geological, and other data related to anthropogenic hazard from different phases of a geothermal project. Up to now, data are related to Soultz-sous-Forêts, Rittershoffen, Vendenheim and Illkirch.

The CDGP was designed (1) as a store to archive and distribute isolated data and (2) as a gateway to access data handled by another datastore. Indeed, other data can be found elsewhere: in official national stores like Minergies [http://www.minergies.fr/en]or InfoTerre [https://infoterre.brgm.fr/], in academic or project-related stores like BCSF-Renass [https://renass.unistra.fr] or GFZ Data Services [https://dataservices.gfz-potsdam.de/portal/], or even using an internal data service like the one provided by the EOST’ seismological data center CDS [https://eost.unistra.fr/plateformes/cds].

A major objective is to give access to data – even outside the CDGP; they are described in metadata records, where links to the resource are set. Access rights can be controlled and granted either by the destination store, or - if requested - by the CDGP. In this latter case, access rules are defined by the center providing data, and access is validated by the CDGP and request is made only if it is granted. Another advantage is to avoid data duplication and therefore disk space, follow-up of updates, access rights management. If possible, these remote data are also provided as the local data to the EPOS Anthropogenic Hazard platform [https://tcs.ah-epos.eu/]

This feature is useful for users who do not need to search for data on several different sites. It is also useful for data providers and centers who wish to make their data known while keeping control of data access, or need to do special actions before giving access to their data.