Open Porous Media (OPM) Flow is an open-source reservoir simulator used for solving subsurface porous media flow problems. Focus is placed here on carbon sequestration and the modeling of fluid flow within underground porous reservoirs. In this study, a sensitivity analysis of some input parameters for carbon sequestration is performed using six different uncertain parameters. An ensemble of model realizations is simulated using OPM Flow, and the model output is then calculated based on the values of the six input parameters mentioned above. CO2 injection is simulated for a period of 15 years, while the post-injection migration of CO2 in the saline storage aquifer is simulated for a subsequent period of 200 years, leading to a final analysis after 215 years. The input parameter values are generated using the quasi-Monte Carlo (QMC) method in the region of interest, following specified patterns suitable for analysis. The optimal convergence rate for quasi-Monte Carlo is observed. The aim of this study is to identify important input parameters contributing significantly to the model output, which is accomplished using sensitivity analysis and verified through symbolic regression modeling based on machine learning. Global sensitivity analysis using the Sobol sequence identifies input parameter 3, 'Permeability of shale between sand layers,' as having the most influence on the model output 'Secondary Trapped CO2.' All regression models, including the simplest and least accurate ones, incorporate parameter 3, confirming its significance. These approximations are valid within the designated area of interest for the input parameters and are easily interpretable for human experts. Sensitivity analysis of the developed time-dependent carbon sequestration model shows that the significance of each physical parameter changes over time: Sand porosity is more significant than shale permeability for roughly the first 120 years. Consequently, the presented results show that simulation timescales of at least 200 years are necessary for carbon sequestration evaluation.

Faculty of Electronic Engineering University of Niš 18000 Niš Serbia

IT4Innovations VSB Technical University of Ostrava 708 00 Ostrava Czech Republic

SINTEF Digital 0373 Oslo Norway

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