监测二氧化碳羽流演化对于确保地质封存的安全性和完整性至关重要。传统的基于数值模拟的数据同化工作流程计算成本高昂，而地质不确定性的纳入更使这一问题雪上加霜，因为要实现稳健的性能预测，必须考虑地质不确定性。因此，考虑地质不确定性的储层模拟和模型校准并不适用于大规模应用中二氧化碳羽流演化的实时监测。本文提出了一种基于深度学习（DL）的方法，该方法能够实现近实时二氧化碳羽流可视化和快速数据同化，并整合多种地质实现，用于预测未来羽流演化和确定审查区域（AOR）。
所提出的深度学习模型以二氧化碳封存项目的可用观测数据作为输入，包括注入井的井底压力、监测井的分布式压力测量值以及监测井的二氧化碳饱和度测井数据。
Monitoring CO2-plume evolution is essential for ensuring geologic storage security and integrity. Traditional numerical simulation-based data-assimilation workflows are computationally expensive, an aspect further complicated by the fact that geologic uncertainty must be incorporated for robust performance prediction. Therefore, reservoir simulation and model calibration accounting for geologic uncertainty are not amenable to real-time monitoring of CO2-plume evolution for large-scale applications. The authors propose a deep-learning (DL)-based approach that enables near-real-time CO2-plume visualization and rapid data assimilation that incorporates multiple geological realizations for predicting future plume evolution and area-of-review (AOR) determination.
The proposed DL model takes available observed data from CO2 sequestration projects as input, including bottomhole pressure at the injection well, distributed pressure measurements at monitoring wells, and CO2-saturation-log data at monitoring wells.
