论文标题
贝叶斯倒置用于二氧化碳地质存储的生成模型
Bayesian Inversion Of Generative Models For Geologic Storage Of Carbon Dioxide
论文作者
论文摘要
碳捕获和储存（CC）可以帮助大气的脱碳，以限制全球温度的升高。利用无监督学习的框架用于生成一系列地下地质体积，以研究潜在的位点，以长期存储二氧化碳。生成的对抗网络用于创建地质体积，进一步的神经网络用于采样训练有素的发电机的后验分布，以进行稀疏的物理测量。这些生成模型进一步调节是通过贝叶斯反转的历史动态流体流量数据，以改善注入二氧化碳的存储容量的预测。
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.
