We propose a quantitative model-based workflow for conformance verification of CO₂ storage projects. Bayesian inference is applied to update an ensemble of simulation models that capture prior uncertainty based on mismatches with measured data. Conformance assessments are derived by comparison of updated model predictions with storage permit requirements and confidence criteria. Two examples, one conceptual and one based on a real candidate storage site, are provided in which the quantitative workflow is applied to the a priori assessment of candidate monitoring strategies. The examples illustrate the limitations of pressure monitoring in the presence of realistic subsurface uncertainties, and the potential for cost saving by informed design of geophysical monitoring surveys. Approximate methods are discussed that could make the workflow also applicable for (quasi) real-time conformance monitoring.

我们提出了一种基于定量模型的工作流程，用于 CO _{2 的}一致性验证存储项目。应用贝叶斯推理来更新模拟模型的集合，这些模型基于与测量数据的不匹配来捕获先验不确定性。一致性评估是通过将更新的模型预测与存储许可要求和置信度标准进行比较得出的。提供了两个示例，一个是概念性的，一个是基于真实候选存储站点的，其中将定量工作流应用于候选监控策略的先验评估。这些例子说明了在存在现实地下不确定性的情况下压力监测的局限性，以及通过地球物理监测调查的知情设计节省成本的潜力。讨论了可以使工作流也适用于（准）实时一致性监控的近似方法。