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Carbon capture and storage (CCS) can aid decarbonization of the atmosphere tolimit further global temperature increases. A framework utilizing unsupervisedlearning is used to generate a range of subsurface geologic volumes toinvestigate potential sites for long-term storage of carbon dioxide. Generativeadversarial networks are used to create geologic volumes, with a further neuralnetwork used to sample the posterior distribution of a trained Generatorconditional to sparsely sampled physical measurements. These generative modelsare further conditioned to historic dynamic fluid flow data through Bayesianinversion to improve the resolution of the forecast of the storage capacity ofinjected carbon dioxide.

Bayesian Inversion Of Generative Models For Geologic Storage Of Carbon Dioxide