Surrogate Model for Geological CO2 Storage and Its Use in Hierarchical MCMC History Matching
This work addresses uncertainty reduction in geological CO2 storage operations, which is crucial for environmental monitoring and risk assessment, though it is incremental as it extends an existing surrogate model to handle a broader range of geological scenarios.
The authors tackled the problem of history matching for geological CO2 storage under high geological uncertainty by developing a deep-learning-based surrogate model, achieving median relative errors of 1.3% in pressure and 4.5% in saturation for new realizations. They integrated this model into a hierarchical MCMC workflow, which substantially reduced geological uncertainty and improved agreement with true-model responses.
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 of log-permeability, permeability anisotropy ratio, and constants in the porosity-permeability relationship. An infinite number of realizations can be generated for each set of metaparameters, so the range of prior uncertainty is large. The surrogate model is trained with flow simulation results, generated using the open-source simulator GEOS, for 2000 random realizations. The flow problems involve four wells, each injecting 1 Mt CO2/year, for 30 years. The trained surrogate model is shown to provide accurate predictions for new realizations over the full range of geological scenarios, with median relative error of 1.3% in pressure and 4.5% in saturation. The surrogate model is incorporated into a hierarchical Markov chain Monte Carlo history matching workflow, where the goal is to generate history matched geomodel realizations and posterior estimates of the metaparameters. We show that, using observed data from monitoring wells in synthetic `true' models, geological uncertainty is reduced substantially. This leads to posterior 3D pressure and saturation fields that display much closer agreement with the true-model responses than do prior predictions.