Bayesian Inversion Of Generative Models For Geologic Storage Of Carbon Dioxide
This work addresses the challenge of site selection for carbon capture and storage, which is crucial for decarbonization efforts, though it appears incremental as it applies existing methods to a specific domain.
The paper tackles the problem of identifying potential sites for long-term carbon dioxide storage by generating subsurface geologic volumes using generative adversarial networks, and improves storage capacity forecasts by conditioning these models on sparse physical measurements and historic fluid flow data through Bayesian inversion.
Carbon capture and storage (CCS) can aid decarbonization of the atmosphere to limit further global temperature increases. A framework utilizing unsupervised learning is used to generate a range of subsurface geologic volumes to investigate potential sites for long-term storage of carbon dioxide. Generative adversarial networks are used to create geologic volumes, with a further neural network used to sample the posterior distribution of a trained Generator conditional to sparsely sampled physical measurements. These generative models are further conditioned to historic dynamic fluid flow data through Bayesian inversion to improve the resolution of the forecast of the storage capacity of injected carbon dioxide.