Scenario-based data assimilation framework to improve production estimates for geologically complex geothermal reservoirs

Geothermal energy has the potential to decarbonize heating, cooling, and power production. However, managing the efficient and sustainable exploitation of geothermal resources is challenging due to the limited data availability, which restricts our ability to characterize and quantify the multi-scale, hierarchical geological structures of the hosting reservoirs. In this study, we propose a scenario-based data assimilation framework that enables the efficient modelling of multiple complex geological scenarios and is linked to flow and heat transfer simulations for subsequent uncertainty analysis. This framework is based on an ensemble smoother with multiple data assimilation (ESMDA) and demonstrated on a channelized fluvial geothermal reservoir. By improving the open-source Rapid Reservoir Modelling (RRM) tool, we efficiently create multiple deterministic fluvial geothermal reservoir scenarios that honors facies along well paths in a probabilistic manner by randomly selecting, cropping, and stacking channelized layers from the layer template library. Petrophysical properties for each scenario are then modelled using geostatistics to generate a geologically plausible and sufficiently diverse ensemble of reservoir realizations. The multiple scenarios and corresponding ensemble realizations are then subjected to heat and fluid flow simulations using the open-source Delft Advanced Research Terra Simulator (open-DARTS) to quantify the uncertainty of production temperatures and reservoir pressures. Finally, ESMDA is employed to assimilate temperature and pressure profiles at the injection well, monitoring borehole, and production well across all members of the ensemble realizations for the different geological scenarios. We demonstrate the applicability of our framework using a synthetic, yet geologically consistent, case study of a low-enthalpy geothermal system where heat is produced from a geothermal doublet located in a channelized fluvial sandstone reservoir. The framework enables the falsification of geological scenarios with poor data assimilation performance that is unlikely to reflect the actual reservoir architecture, and supports the identification of plausible geological scenarios that are more likely to represent the subsurface geology based on the deviation of modelled and observed well temperature and pressure profiles. The workflow offers an efficient way to constrain geological uncertainties inherent to geologically complex geothermal reservoirs and improve the forecasting of production temperatures and pressure differences. • Geothermal reservoirs involve higher uncertainty due to limited data availability. • Scenario-based geological modeling combined with probabilistic property modelling captures key uncertainties in geologically complex geothermal reservoirs. • Reservoir scenarios with poor data match are falsified using scenario-based ESMDA. • Reliable production forecasts are achieved via plausible scenarios, not a biased base case.