Ensemble Data Space Inversion for Fast CO2 Injection Forecast Evaluation

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Summary The geological storage of CO2 into depleted reservoirs represents a potential strategy for large-scale greenhouse gas mitigation. An evaluation of the storable volume and of the behaviour of the injected fluid considering the uncertainty is essential to mitigate the associated geo-mechanical risks and the potential CO2 leakage. The accurate estimation of the quantity of CO2 that can be injected into a depleted reservoir is usually carried out after the calibration of the reservoir model. This model calibration can be addressed via a history matching process, by assimilating production and pressure data collected during the field production history. The prior uncertainties represented by an ensemble of reservoir model realizations are thus reduced, by solving a nonlinear inverse problem with computationally demanding methods such as iterative ensemble data assimilation. The history matching phase is crucial for the forecast simulation under realistic conditions of carbon capture and storage applications, but it can be time consuming especially in presence of a long historical time. In the present work, we propose an innovative method to significantly reduce the computational impact of the calibration process, adopting a direct forecasting approach based on the ensemble data space inversion (DSI) introduced by Lima et al. [ 1 ]. With this approach, a direct prior ensemble prediction update is performed to account for the historical data, without modifying the models themselves. The Posterior (history-matched) geological models are not explicitly obtained as in standard model-space inversion methods and as consequence the requirements in term of computational resources and CPU time are strongly reduced. The DSI approach is here implemented adopting an iterative ensemble smoother formulation tailored to quantify the uncertainty on the total volume of CO2 that can be stored into the depleted reservoir. Moreover, the method can be used to assess the uncertainty of the subsurface fluid flow. The application examples show that a calibration process via the DSI algorithm can produce accurate forecast predictions and can consistently reduce uncertainty. The results are comparable to the ones that are obtained by standard model-space inversion approaches.