Risk assessment of ${\mathrm{CO}}_2$ storage requires the use of geophysical monitoring techniques to quantify changes in selected reservoir properties such as ${\mathrm{CO}}_2$ saturation, pore pressure, and porosity. Conformance monitoring and the associated decision making rest upon the quantified properties derived from geophysical data, with uncertainty assessment. We have developed a general framework combining seismic and controlled-source electromagnetic (CSEM) inversions with rock-physics inversion with fully Bayesian formulations for proper quantification of uncertainty. The Bayesian rock-physics inversion rests upon two stages. First, a search stage consists of exploring the model space and deriving models with the associated probability density function (PDF). Second, an appraisal or importance sampling stage is used as a “correction” step to ensure that the full model space is explored and that the estimated posterior PDF can be used to derive quantities such as marginal probability densities. Both steps are based on the neighborhood algorithm. The approach does not require any linearization of the rock-physics model or assumption about the model parameters’ distribution. After describing the ${\mathrm{CO}}_2$ storage context, the available data at the Sleipner field before and after ${\mathrm{CO}}_2$ injection (baseline and monitor), and the rock-physics models, we perform an extended sensitivity study. We find that prior information is crucial, especially in the monitor case. We determine that joint inversion of seismic and CSEM data is also key to properly quantifying ${\mathrm{CO}}_2$ saturations. Finally, we apply the full inversion strategy to real data from Sleipner. We obtain rock frame moduli, porosity, saturation, and patchiness exponent distributions and the associated uncertainties along a 1D profile before and after injection. The results are consistent with geology knowledge and reservoir simulations, i.e., that the ${\mathrm{CO}}_2$ saturations are larger under the caprock confirming the ${\mathrm{CO}}_2$ upward migration by buoyancy effect. The estimates of the patchiness exponent have a larger uncertainty, suggesting semipatchy mixing behavior.

中文翻译：

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贝叶斯岩石物理反演：应用于二氧化碳封存监测

风险评估 ${\mathrm{二氧化碳}}_2$ 存储需要使用地球物理监测技术来量化选定储层特性的变化，例如 ${\mathrm{二氧化碳}}_2$饱和度、孔隙压力和孔隙度。一致性监测和相关决策依赖于从地球物理数据得出的量化特性，以及不确定性评估。我们开发了一个通用框架，将地震和受控源电磁 (CSEM) 反演与岩石物理反演与完全贝叶斯公式相结合，以适当量化不确定性。贝叶斯岩石物理反演基于两个阶段。首先，搜索阶段包括探索模型空间并使用相关的概率密度函数 (PDF) 导出模型。其次，评估或重要性采样阶段用作“校正”步骤，以确保探索完整的模型空间，并且估计的后验 PDF 可用于导出诸如边际概率密度之类的数量。这两个步骤都基于邻域算法。该方法不需要对岩石物理模型进行任何线性化或对模型参数分布进行假设。描述完之后${\mathrm{二氧化碳}}_2$ 存储上下文，Sleipner 字段之前和之后的可用数据 ${\mathrm{二氧化碳}}_2$注入（基线和监测）和岩石物理模型，我们进行了扩展的敏感性研究。我们发现先验信息至关重要，尤其是在监控案例中。我们确定地震和 CSEM 数据的联合反演也是正确量化的关键${\mathrm{二氧化碳}}_2$饱和度。最后，我们将完全反演策略应用于来自 Sleipner 的真实数据。我们获得了注入前后沿一维剖面的岩石框架模量、孔隙度、饱和度和斑块指数分布以及相关的不确定性。结果与地质知识和储层模拟一致，即${\mathrm{二氧化碳}}_2$ 盖层下的饱和度更大，证实了 ${\mathrm{二氧化碳}}_2$通过浮力效应向上迁移。斑块指数的估计具有较大的不确定性，表明半斑块混合行为。