
Geothermal development in Europe faces challenges due to scarcity of subsurface information, particularly in densely populated urban areas, where data acquisi-tion is more difficult. Traditional methods for predict-ing reservoir properties depend on high data density and spatial interpolation through geostatistical ap-proaches. The Horizon-Europe funded project GO-Forward seeks to facilitate a paradigm shift in this field by utilizing a coupled workflow of geological forward modelling techniques, comprising stratigraphic, diagenetic, fault and fracture modelling. This innovative approach al-lows for the physics-based modelling and prediction of reservoir heterogeneities in three dimensions, making it possible to derive valuable insights even in areas with limited data availability. By analysing the regional ge-ological history of reservoir rocks, GO-Forward simu-lates the geological processes that have shaped these systems over time, thereby improving the accuracy of pre-drilling predictions. The project integrates existing simulation tools and methods that have been previously applied in hydrocar-bon exploration and adapts them to the complexities of geothermal environments. This comprehensive model-ling framework not only enhances the understanding of geothermal plays but also significantly reduces pre-drill risk associated with exploration. The methods will be validated in regions with abundant subsurface infor-mation and production data. Once calibrated, the mod-els will be utilized in greenfield areas, supporting the de-risking of geothermal exploration in these locations. GO-Forward emphasizes a combination of technology options tailored to the diverse geological settings repre-sentative of various geothermal plays across Europe, ultimately contributing to a more efficient and effective geothermal energy rollout.
Machine Learning, Stratigraphic Forward Modelling, Fracture Forward Modelling, Exploration, Diagenetic Forward Modelling
Machine Learning, Stratigraphic Forward Modelling, Fracture Forward Modelling, Exploration, Diagenetic Forward Modelling
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