论文标题
实时高分辨率CO $ _2 $使用嵌套傅里叶神经操作员地质存储预测
Real-time high-resolution CO$_2$ geological storage prediction using nested Fourier neural operators
论文作者
论文摘要
碳捕获和存储（CCS）在全球脱碳中起着至关重要的作用。扩展CCS部署需要对存储储层压力积累和气体羽流迁移的准确和高分辨率建模。但是，由于现有数值方法的高计算成本，这种建模在大规模上非常具有挑战性。这项挑战导致评估存储机会的严重不确定性，这可能会延迟大规模CCS部署的步伐。我们介绍了嵌套的傅立叶神经操作员（FNO），这是一种机器学习框架，用于在盆地尺度上用于高分辨率动态3D CO2存储建模。与现有方法相比，嵌套的FNO使用FNOS的层次结构和速度提高流量预测的速度近700,000倍，以不同的改进水平产生预测。嵌套FNO通过学习控制部分微分方程的解决方案操作员，创建了一种通用数值模拟器，用于具有多种储层条件，地质异质性和注入方案的二氧化碳存储替代方案。我们的框架实现了前所未有的实时建模和概率模拟，可以支持全球CCS部署的扩展。
Carbon capture and storage (CCS) plays an essential role in global decarbonization. Scaling up CCS deployment requires accurate and high-resolution modeling of the storage reservoir pressure buildup and the gaseous plume migration. However, such modeling is very challenging at scale due to the high computational costs of existing numerical methods. This challenge leads to significant uncertainties in evaluating storage opportunities, which can delay the pace of large-scale CCS deployment. We introduce Nested Fourier Neural Operator (FNO), a machine-learning framework for high-resolution dynamic 3D CO2 storage modeling at a basin scale. Nested FNO produces forecasts at different refinement levels using a hierarchy of FNOs and speeds up flow prediction nearly 700,000 times compared to existing methods. By learning the solution operator for the family of governing partial differential equations, Nested FNO creates a general-purpose numerical simulator alternative for CO2 storage with diverse reservoir conditions, geological heterogeneity, and injection schemes. Our framework enables unprecedented real-time modeling and probabilistic simulations that can support the scale-up of global CCS deployment.
