The Horizon Europe CRM-geothermal project: Deliverable 2.5 - Report on resource calculation according to the UNFC

This report uses a case study to demonstrate how the UNFC-2009 reporting standards can be adopted to the co-generation of electricity and heat with CRM (critical raw materials). This helps to link to work package 4 of the CRM-geothermal project, which focuses on 'The application of CRM and combined energy recovery from geothermal fluids'. The joint application of the UNFC classification system to geothermal energy utilisation and CRM extraction offers considerable benefits for project integration, investment confidence, and sustainable resource governance. Although challenges remain - especially in aligning resource maturity levels and operational timelines - UNFC’s flexibility and comprehensive scope make it a valuable tool for current and future hybrid resource developments. According to the case study and laboratory tests carried out as part of the CRM-Geothermal Project, one of the most important things to examine is the kinetics of the process under reservoir conditions, i.e. how quickly the minerals lining the fractures dissolve. In general, hydrodynamic and transport modelling are appropriate tools for the overall evaluation of the multiple projects according to the UNFC code. The joint assessment must be based on the value of each commodity, which is calculated separately according to its UNFC code. However, it must also assess the environmental, social and technical feasibility of all products. In all cases, the weakest category should be considered the common value. From an economic point of view, it should be considered whether the project as a whole can still be considered economically viable if the production of heat and electricity alone makes it viable, despite a possibly uneconomic nature of CRM production. It is important to note that large datasets, particularly those from hydrocarbon research, facilitate the application of a wide range of machine learning methods. This is particularly true of comprehensive petrophysical datasets and traditional geostatistical methods that were previously carried out using them. As the calculation of recoverable geothermal energy is similar to that in the hydrocarbon industry in many respects, a breakthrough in the application of machine learning is expected soon.