2020/01/08 by Gavin H. Graham, Yan Chen, Graham, Gavin H. +1
Engineering · Environmental Science · #CO2 Sequestration and Geologic Interactions #FOS: Computer and information sciences #Hydraulic Fracturing and Reservoir Analysis #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Reservoir Engineering and Simulation Methods
paper · pdf · doi:10.48550/arxiv.2001.04829
openalex publication_date 2020/01/08 · openalex created_date 2020/01/23 · openalex updated_date 2026/07/28
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.