利用集合卡尔曼滤波对二氧化碳羽流动态进行地震监测

Grant Bruer, Abhinav Prakash Gahlot, Edmond Chow, Felix Herrmann
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引用次数: 0

摘要

对注入和储存在地下储层中的二氧化碳(CO2)进行监测,对于避免出现故障情况和实时优化二氧化碳注入率至关重要。序列贝叶斯数据同化(DA)是一种统计方法,用于结合来自多个来源的长期信息来估计隐藏状态,如地下二氧化碳羽流的扩散。集合卡尔曼滤波器(EnKF)就是可扩展的高效序列贝叶斯数据同化的一个例子。我们将这种可扩展的贝叶斯算法应用于高维 CO2 储层,使用两相流动力学和具有现实地表地震勘测设计的延时全波形地震数据,从而改进了地震-CO2 监测领域现有的贝叶斯算法。与单独使用地震数据或流体物理数据相比,我们使用 EnKF 对二氧化碳饱和度场进行了更精确的估算。此外,我们还测试了一系列 EnKF 超参数值,并为二氧化碳储层地震监测的参数选择提供了指导。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Seismic monitoring of CO2 plume dynamics using ensemble Kalman filtering
Monitoring carbon dioxide (CO2) injected and stored in subsurface reservoirs is critical for avoiding failure scenarios and enables real-time optimization of CO2 injection rates. Sequential Bayesian data assimilation (DA) is a statistical method for combining information over time from multiple sources to estimate a hidden state, such as the spread of the subsurface CO2 plume. An example of scalable and efficient sequential Bayesian DA is the ensemble Kalman filter (EnKF). We improve upon existing DA literature in the seismic-CO2 monitoring domain by applying this scalable DA algorithm to a high-dimensional CO2 reservoir using two-phase flow dynamics and time-lapse full waveform seismic data with a realistic surface-seismic survey design. We show more accurate estimates of the CO2 saturation field using the EnKF compared to using either the seismic data or the fluid physics alone. Furthermore, we test a range of values for the EnKF hyperparameters and give guidance on their selection for seismic CO2 reservoir monitoring.
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