Bayesian inversion of multilayer $\mathrm{CO}_2$ migration from seismic plume observations using a graph-based finite-rate invasion-percolation model
Vertical migration of $\mathrm{CO}_2$ in layered sandstone reservoirs is controlled by thin shale barriers whose properties are often poorly known. We develop a Bayesian framework that uses time-lapse seismic plume observations to estimate effective parameters governing lateral and vertical $\mathrm{CO}_2$ migration. The forward model is a fast graph-based invasion-percolation model that extends conventional IP by representing both capillary-controlled filling of structural traps beneath shale barriers and finite-rate transfer through them. Parameters are inferred with approximate Bayesian computation and sequential Monte Carlo sampling, and the posterior samples are propagated into forecasts. Applied to real data from Sleipner, posterior simulations reproduce the broad distribution of $\mathrm{CO}_2$ across nine sand units and its redistribution between 2010 and 2023. In contrast, the quasi-static model fails to reproduce this temporal evolution. Complementary synthetic experiments assess parameter recovery, forecasting, monitoring duration, and model misspecification. These experiments show that the information gained from monitoring depends on the migration events captured, with breakthrough and post-breach redistribution providing particularly strong constraints. This combination of fast simulation, probabilistic updating, and interpretable effective parameters makes the framework well suited to repeated forecast revision during active injection, especially when full-physics inference is too computationally demanding.