Veranstaltungsprogramm

Deep geothermal energy is a key to lower local and global CO2 emissions caused by the burning of fossil fuels. Different initiatives aim at establishing deep geothermal energy production at the Weisweiler coal-fired power plant near the city of Aachen, Germany, in order to replace district heat generated as a side product of coal burning. But how much information do we actually have about or need of the subsurface to carry out such a project?

The conducted investigations will provide a 3D geological and probabilistic subsurface model of the area between Aachen and Weisweiler created with the open-source package GemPy developed at RWTH Aachen University. This model is in contrast to established regional models and more detailed local models.

The geological structures between Aachen and Weisweiler represent a SW-NE striking syncline, the Inde Syncline, embedded in the Aachen fold-and-thrust belt. The syncline is offset by Cenozoic normal faults of the Lower Rhine Embayment. The target layers comprise of karstic Lower Carboniferous Kohlenkalk platforms and Upper/Middle Devonian Massenkalk reef carbonates outcropping along the flanks and down faulted within the Lower Rhine Embayment.

Results show that the Aachen fold-and-thrust belt and the down faulted fault blocks can be modeled integrating the available surface and sparse shallow subsurface data. The probabilistic modeling provides information about uncertainties of the target layers in the subsurface. It can be deduced that a planned exploration well for fall/winter 2021 will reduce uncertainties in the subsurface in the vicinity of the target layers enabling improved economic decisions.

The geothermal potential of the North German Basin has been under evaluation since the early 1970s. Results have shown significant amounts of geothermal resources bound to Palaeozoic and Mesozoic reservoirs. But mainly for economic reasons, the exploration and development has been focused on Mesozoic hydrothermal sandstone reservoirs.

The fluvio-deltaic sandstones of the Upper Triassic Exter Formation, has been a successful geothermal target for direct use with significant porosity in this basin. The long-term operations at Neubrandenburg, Neustadt-Glewe und Waren, and the recently successful development at Schwerin demonstrate the relevance of Rhaetian sandstone reservoirs for the increasing direct use of geothermal energy in North Germany. Nevertheless, the potential of this sedimentary complex has barely been exploited. The high exploration risk due to a lack of information, such as reservoir geometry, thickness, depth and petrophysical properties hampered the reservoir development at other sites. Previous studies have contributed to improve reservoir prediction of Rhaetian reservoirs by basin- to regional-scale subsurface mapping of facies and relevant reservoir parameters based on sedimentological methods. However, a validated seismic methodology to delineate the reservoir geometry and reservoir quality on the local scale is missing.

The aim of this research is to propose a validated seismic facies analysis approach for the interpretation of 2D and 3D seismic data sets to enable comprehensive reservoir predictions at individual localities. A further goal of this study will focus on the application of Machine Learning algorithms to identify and classify fluvio-deltaic facies in seismic data. The preliminary results at the current stage of this research are hereby presented, corresponding to the identification of the Rhaetian complex by means of seismic attributes analysis applied to 3D-seismic data. An interactive interpretation workflow, as used in the hydrocarbon exploration, has been applied to recognize the distribution and geometry of fluvio-deltaic reservoirs.

Based on the obtained results, it is confirmed that fluvio-deltaic channels are present in the middle Exter Formation (“Contorta-Sandstein”) in eastern Lower Saxony. The channel distribution found so far based on seismic attributes, evidences avulsion and lateral shifting of individual channels contributing to the formation of channel belts within the Rhaetian deltaic system as proposed by subsurface mapping. These results significantly contribute to reduce the geothermal exploration risk in North German Basin by presenting a way to enhance the reliability of prediction of the Rhaetian reservoirs based on seismic methods.

The success of an Enhanced Geothermal System (EGS) relies on the creation of a fracture network a geometry that facilitates the fluid flow between boreholes and avoids interaction with in-situ faults. Engineering the stimulation of the suitable fracture network requires careful planning that typically involves modelling of the fracture growth based on local stress magnitude and orientation, reservoir rheological and fluid conductivity properties and characteristic of the in-situ Discrete Fracture Network (DFN). However, the impact of heterogeneity, local stress shadows and rotation and the fracturing process itself can result in unpredicted deviations from the target objectives. Monitoring of the microseismic activity associated to the induced fractures provides an essential feedback in EGS operations by imaging the fracturing process as it develops. A valuable information extracted from the microseismic catalogue is the interpretation of the geometry of the induced fracture network.

This study presents a process to automate and remove observer’s subjectivity in the detection and characterisation of developing fractures. For this purpose we analysed a catalogue of data from the U.S. DoE’s EGS Collab project (https://www.energy.gov/eere/geothermal/egs-collab) recorded during repeated hydraulic stimulation experiments at Sandford Underground Research Facility at an approximate depth of 1.5 km below surface and processed by Schoenball et al. (2019).

The microseismic catalogue consists of 1,933 events recorded by a dense 3D network of two strings of 12 hydrophones and 18 three-component accelerometers surrounding the injection volume.

The events appear in distinct spatial clusters as indicated by the pair analysis and quantified by the degree of spatial randomness. The deviation in interevent separation from what would be obtained for a uniform random distribution observed at short intervent distances is typically observed in induced seismicity or aftershock series. Following this observation, in order to automate the analysis of the underlying structure within the induced fracture, the seismic catalogue is split into different clusters identified using a k-means clustering approach , optimising the number of clusters using a proximity regrouping of the starting number.

The geometry of the induced and mobilized fracture network is done through a statistical approach applying the three-point method (e.g. Reyes-Montes & Young 2006, Fehler et al. 1987), automatically calculating the orientation of planes that fit every unique combination of three events. By plotting the poles of the calculated planes on a stereogram, preferential orientations are highlighted by areas of high density. The analysis was applied to the complete dataset and to each individual cluster identified in the previous step. The results for all clusters show clearly identifiable preferential orientations, interpreted as the orientation of the macro-fractures formed by multiple microcracks that describe a macroscopic active Discrete Fracture Network. The overall dominating structure corresponded to subvertical planes oriented along the maximum horizontal stress, however shallow dipping structures were also identified that could correspond to the in-situ fabric. Other orientations present in the dataset would require further investigation as could respond to local stress rotations or be an effect of the rock heterogeneity. The results are consistent with those observed by Schoenball et al. (2019) and validate the approach as a real-time automatic tool for the monitoring of fracture development.

The Earth's subsurface holds natural resources which are fundamental for regional development. Obtaining accurate images of water, mineral, and energy sources deep below the surface is a crucial step for their management and exploitation. Geophysical imaging allows us to obtain detailed maps of the Earth's interior. This is achieved by analyzing the deformations and electromagnetic (EM) fields measured at the surface. The EM modeling/inversion routines predict the EM fields arising from induced electric currents in the Earth's subsurface. The EM response to that excitation source depends on the electrical distribution of geological properties. From this dependence, it is possible to extract useful subsurface information to improve and reinforce reservoirs' characterization and interpretation.

In this conference, we present the role of a workflow for detecting and characterizing energy reservoirs based upon EM methods and High-performance Computing (HPC). The numerical experiments are focused on the Vallès fault (Northeast, Spain), where several geothermal anomalies have been identified/observed, and different geophysical surveys have been carried out to characterize its deeper reservoirs. However, the setting remains poorly understood and primarily untapped. Furthermore, the granite bedrock with a highly fractured nature represents a significant challenge for its geological study and numerical modeling. At the same time, its potential for heat generation has brought tremendous interest, with a nearby location to urban areas.

We describe the experimental setup for the Vallès region, a 2-D joint EM inverse model, several 3-D EM simulations, and their comparison with real data measurements. The experiments included in this talk show how to use numerical simulations to study realistic problems in a geothermal exploration context. Furthermore, these results depict that a triple helix approach based on novel numerical strategies, EM methods, and HPC can be extremely competitive for the solution of realistic and complex 3D models in the geothermal energy context.

To achieve the European Commission goal to have a green, and sustainable growth, it is necessary to research and develop techniques for clean energy production. In particular, the development of new practical, reliable methods for geophysical characterization of a reservoir can promote and facilitate the application of deep geothermal energy. Measuring data in a harsh environment, such as elevated temperatures requires the employment of specific technologies such as application of optical cables. With these cables a detailed image of the subsurface can be created, using for instance seismic imaging techniques. In addition, the deployment is more cost-efficient in terms of time and costs, in comparison with standard installation of conventional borehole instruments. Therefore, developing fibre optics surveying could be very promising for this purpose.

The Groß-Schönebeck in-situ laboratory is situated in the North German Basin, and is one of Germany's three major type localities for deep geothermal energy. Within the framework of the joint research project RissDom-A (RissDominierte Erschließung in German: fracture-dominated exploitation) in early 2017, a vertical seismic profile (VSP) survey using wireline Distributed Acoustic Sensing (DAS) technology was carried out. As a result, unique borehole measurements were recorded in the two 4.3 km deep wells E GrSk 3/90 and Gt GrSk 4/05 with 5 m spatial sampling. The 61 vibroseis source points had various offsets from 200 to 2000 meters with a spiral layout around the target area to ensure a good azimuth distribution.

With these measurements, a detailed 3-dimensional image around the existing boreholes was created. An interpretation of the generated DAS VSP cube provides information previously unknown for the Groß-Schönebeck site. For the first time, borehole seismic imaging was able to resolve a complex thin interlaying in the upper part of the Rotliegend reservoir with numerous pinch outs in the depth range from 3.8 to 4.0 km. In addition, the high resolution of the data allowes to trace depth variations of the Elbe basis sandstone horizon at 4.08-4.10 km depth, which is one of the possible targets for the future explorations plans of the research site. Moreover, the 3D VSP cube evidences the existence of an unconformity in the area where we expect volcanic rocks. Thereby, the borehole seismic imaging results will shape the further development of the Groß Schönebeck in-situ geothermal laboratory by resolving small-scale features in the reservoir.

The geological setting at the experiment site is typical for a broad part of Northern Europe. Therefore, the acquired knowledge from this case study can be applied for geothermal exploration programmes in other areas with similar geological conditions.