论文标题
用于储层监控的差异自动编码器
A Variational Auto-Encoder for Reservoir Monitoring
论文作者
论文摘要
二氧化碳捕获和存储（CCS）是减轻人为CO $ _2 $排放的重要策略。为了使CC成功，必须存储大量CO $ _2 $，并且必须监视存储站点的符合。在这里，我们提出了一种深度学习方法，以根据上述区域监测间隔（AZMI）井的压力数据从存储形成中重建压力场并将其分类。深度学习方法是针对解决两个任务的半条件变量自动编码器的一个版本：重建增量压力场和泄漏率分类。从高保真异质性的2D数值储层模型的合成数据上说明了方法，预测和相关的不确定性估计值，该模型用于模拟地下CO $ _2 $移动和由于CO $ _2 $泄漏而引起的AZMI的压力变化。
Carbon dioxide Capture and Storage (CCS) is an important strategy in mitigating anthropogenic CO$_2$ emissions. In order for CCS to be successful, large quantities of CO$_2$ must be stored and the storage site conformance must be monitored. Here we present a deep learning method to reconstruct pressure fields and classify the flux out of the storage formation based on the pressure data from Above Zone Monitoring Interval (AZMI) wells. The deep learning method is a version of a semi conditional variational auto-encoder tailored to solve two tasks: reconstruction of an incremental pressure field and leakage rate classification. The method, predictions and associated uncertainty estimates are illustrated on the synthetic data from a high-fidelity heterogeneous 2D numerical reservoir model, which was used to simulate subsurface CO$_2$ movement and pressure changes in the AZMI due to a CO$_2$ leakage.
