Surrogate Model for Geological CO2 Storage and Its Use in Hierarchical MCMC History Matching

Deep-learning-based surrogate models show great promise for use in geological carbon storage operations. In this work we target an important application - the history matching of storage systems characterized by a high degree of (prior) geological uncertainty. Toward this goal, we extend the recently introduced recurrent R-U-Net surrogate model to treat geomodel realizations drawn from a wide range of geological scenarios. These scenarios are defined by a set of metaparameters, which include the horizontal correlation length, mean and standard deviation …

application carbon co2 cs.cv hierarchical history mcmc operations physics.geo-ph prior show storage systems uncertainty work