基于InSAR地表位移数据同化的深度学习加速三维碳储层压力预测
Deep Learning-Accelerated 3D Carbon Storage Reservoir Pressure Forecasting Based on Data Assimilation Using Surface Displacement from InSAR
摘要：
通过同化监测数据快速预测地质碳封存储层压力分布是一个具有挑战性的问题。由于高钻井成本，GCS项目通常具有来自井的空间稀疏测量，导致储层压力预测的高不确定性。为了应对这一挑战，我们建议使用低成本的干涉合成孔径雷达（InSAR）数据作为监测数据来推断储层压力的建立。我们开发了一个深度学习加速的工作流程，以同化从InSAR解释的地表位移图，并预测动态储层压力。该工作流程采用Encourage Smoother Multiple Data Assimilation（ES-MDA）框架，更新三维（3D）地质属性，并预测具有量化不确定性的储层压力。我们使用一个合成的商业规模的GCS模型双峰分布的渗透率和孔隙度，以证明工作流程的有效性。一个两步CNN-PCA方法被用来参数化的双峰字段。工作流程的计算效率通过两个基于剩余U-Net的代理模型分别用于地表位移和储层压力预测来提高。该工作流程可在个人计算机上在半小时内完成数据同化和储层压力预测。
英文摘要：
Fast forecasting of reservoir pressure distribution in geologic carbon storage (GCS) by assimilating monitoring data is a challenging problem. Due to high drilling cost, GCS projects usually have spatially sparse measurements from wells, leading to high uncertainties in reservoir pressure prediction. To address this challenge, we propose to use low-cost Interferometric Synthetic-Aperture Radar (InSAR) data as monitoring data to infer reservoir pressure build up. We develop a deep learning-accelerated workflow to assimilate surface displacement maps interpreted from InSAR and to forecast dynamic reservoir pressure. Employing an Ensemble Smoother Multiple Data Assimilation (ES-MDA) framework, the workflow updates three-dimensional (3D) geologic properties and predicts reservoir pressure with quantified uncertainties. We use a synthetic commercial-scale GCS model with bimodally distributed permeability and porosity to demonstrate the efficacy of the workflow. A two-step CNN-PCA approach is employed to parameterize the bimodal fields. The computational efficiency of the workflow is boosted by two residual U-Net based surrogate models for surface displacement and reservoir pressure predictions, respectively. The workflow can complete data assimilation and reservoir pressure forecasting in half an hour on a personal computer.
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