Periodic Reporting for period 1 - URGENT (Sustainable and affordable URban Geothermal Exploration Novel Technologies and workflows)
Reporting period: 2024-06-01 to 2025-07-31
Europe needs URGENT solutions to decarbonize its heating sector. Geothermal energy can play a prominent role in this transition, providing heat in urban areas as an alternative to fossil fuels. However, its growth is hampered by the limited knowledge about the resources and risks in these challenging environments. Existing exploration methods are not suited for reservoir characterization in urban contexts. The URGENT project addresses this gap by providing sustainable and affordable solutions for urban seismic exploration of geothermal resources. Low-impact innovative technologies, consisting of an electric seismic source and novel MEMS based sensors integrated into autonomous nodes, will be designed, built, and tested on 3 sites: Balmatt (BE), Konin (PL), Batta (HU). They will enable high-quality data recording including low frequency signals resulting in high resolution imaging up to 4000m of depth. Ultimately, improved reservoir characterization will reduce the geological uncertainties and contribute to increase drilling success rate by 20%. Also, by optimizing survey design, including compressive sensing and simultaneous shooting, URGENT eventually aims at reducing exploration costs by 30%. For more efficient analysis of the acquired data, AI & ML methods will be developed and tested to assist and automate fault and fracture detection. Structural models including these features will be the basis for geomechanical modelling to assess fault reactivation and the corresponding seismic risk. In addition, ML techniques used in the O&G sector will be updated and combined with THM reservoir modelling to optimize the location and design of wells, maximizing heat extraction while minimizing seismic risk leading to increase plant lifetime up to 40 years and higher revenue. URGENT will contribute to accelerate the deployment of urban geothermal projects through exploration workflows suited for these environments, market uptake analysis and reinforced community acceptance. The URGENT consortium consists of 8 partners from 5 European countries with complementary expertise who join forces for this 42-month project.
• Data collection: Comprehensive information on the three test sites—Mol (BE), Konin (PL), and Batta (HU)—has been collected and reported to support the survey design and technology development.
• Development of prototypes: User requirements and technical specifications of the technologies have been defined and reported. Design concepts of both the “Synchro-Storm” eVibe electric seismic source and MEMS based receivers have then been made followed by detailed manufacturing designs of the respective prototypes. Production activities are underway with most of the long lead-time items ordered and in production. Sub-component assembly and testing for both the receivers and source is currently being conducted.
• Survey design: Data collection is complete for all sites—Mol (BE), Konin (PL), and Batta (HU)—and surveys are being planned within project constraints. 3D and 2D designs aimed at optimizing data acquisition are under review. At VITO, the 2024 3D dataset was reprocessed with varying decimation and compressive sensing to test sensor reduction while preserving resolution. The main achievement in survey design is the validation of a novel algorithm developed to adaptively optimize survey geometry in complex geological settings, demonstrating strong performance in deep imaging through tailored acquisition designs. The workflow applied to the VITO dataset splits the subsurface into three imaging zones based on depth and signal complexity, showing that aggressive data reduction is viable for shallow zones, while deeper, structurally complex targets require denser, carefully optimized geometries. These findings support the scalability and efficiency of the compressive sensing approach for geothermal and deep subsurface applications.
• AI/Machine Learning workflow: A new ‘paint’ feature for tracing fault sticks was added to OpendTect to enable much easier and faster training of ML models within the software. Additional multiprocessor calculations were also added to the software to further speed up processing time.
• Geothermal plant Risk Management Toolbox: The well builder algorithm was built for the Risk Management Toolbox. The reservoir simulations with DARTS have been coupled with an analytical stress analysis method for fault stress evaluation. The Toolbox is published on VITO’s github space and a minimum viable product has been achieved.
• Development of prototypes: User requirements and technical specifications of the technologies have been defined and reported. Design concepts of both the “Synchro-Storm” eVibe electric seismic source and MEMS based receivers have then been made followed by detailed manufacturing designs of the respective prototypes. Production activities are underway with most of the long lead-time items ordered and in production. Sub-component assembly and testing for both the receivers and source is currently being conducted.
• Survey design: Data collection is complete for all sites—Mol (BE), Konin (PL), and Batta (HU)—and surveys are being planned within project constraints. 3D and 2D designs aimed at optimizing data acquisition are under review. At VITO, the 2024 3D dataset was reprocessed with varying decimation and compressive sensing to test sensor reduction while preserving resolution. The main achievement in survey design is the validation of a novel algorithm developed to adaptively optimize survey geometry in complex geological settings, demonstrating strong performance in deep imaging through tailored acquisition designs. The workflow applied to the VITO dataset splits the subsurface into three imaging zones based on depth and signal complexity, showing that aggressive data reduction is viable for shallow zones, while deeper, structurally complex targets require denser, carefully optimized geometries. These findings support the scalability and efficiency of the compressive sensing approach for geothermal and deep subsurface applications.
• AI/Machine Learning workflow: A new ‘paint’ feature for tracing fault sticks was added to OpendTect to enable much easier and faster training of ML models within the software. Additional multiprocessor calculations were also added to the software to further speed up processing time.
• Geothermal plant Risk Management Toolbox: The well builder algorithm was built for the Risk Management Toolbox. The reservoir simulations with DARTS have been coupled with an analytical stress analysis method for fault stress evaluation. The Toolbox is published on VITO’s github space and a minimum viable product has been achieved.
• The MEMS accelerometers have proven 10 ng/√Hz sensitivity performance which is world-leading for this class of device. Ongoing testing and development are required to ensure similar performance is maintained within the complete seismic receiver electronics architecture and enclosure, and field demonstrations to ensure market adoption. Increased receiver sensitivity means seismic images are sharper, can illuminate deeper targets and improve fault detection accuracy.
• The “Synchro-Storm” eVibe under development will be the largest electromagnetic seismic source in production. The increased power and high signal fidelity allows deeper targets and improved image quality. The low acoustic noise generated by the eVibe means smaller environmental footprint and less disturbance. Surveys can be conducted during the night without disturbing residents, even in urban environments. The ongoing development and field testing is needed to ensure market adoption.
• The integration of machine learning techniques for fault detection, coupled with advanced and accelerated training methods, constitutes a distinctive capability available in OpendTect.
• In the Risk Management Toolbox, the well-builder can automatically build realistic deviated well trajectories and couple those with reservoir simulation to minimize fault reactivation risk (by Coulomb stress criterion) through adjustment of well placement, perforation intervals and operational conditions while searching for the greatest heat production possible.
• The impact of integrating compressive sensing into innovative survey design workflows is currently being tested and validated using a recently acquired 3D dataset at the VITO site. Preliminary results indicate significant potential, with the possibility of reducing the number of receivers and shot points by up to 30% while maintaining seismic data quality. This innovative approach promises to meet objectives with less effort and has the potential to advance the current state of the art.
• The “Synchro-Storm” eVibe under development will be the largest electromagnetic seismic source in production. The increased power and high signal fidelity allows deeper targets and improved image quality. The low acoustic noise generated by the eVibe means smaller environmental footprint and less disturbance. Surveys can be conducted during the night without disturbing residents, even in urban environments. The ongoing development and field testing is needed to ensure market adoption.
• The integration of machine learning techniques for fault detection, coupled with advanced and accelerated training methods, constitutes a distinctive capability available in OpendTect.
• In the Risk Management Toolbox, the well-builder can automatically build realistic deviated well trajectories and couple those with reservoir simulation to minimize fault reactivation risk (by Coulomb stress criterion) through adjustment of well placement, perforation intervals and operational conditions while searching for the greatest heat production possible.
• The impact of integrating compressive sensing into innovative survey design workflows is currently being tested and validated using a recently acquired 3D dataset at the VITO site. Preliminary results indicate significant potential, with the possibility of reducing the number of receivers and shot points by up to 30% while maintaining seismic data quality. This innovative approach promises to meet objectives with less effort and has the potential to advance the current state of the art.