Improved scientific and engineering understanding of the behavior of geologic CO₂ storage together with established regulatory framework and incentive structures raise the prospects for accelerated, large‐scale deployment of this greenhouse gas emissions reduction approach. Incentive structures call for the establishment of appropriate verification and accounting approaches to support claims of the integrity of a geologic storage complex and to justify taking credit for long‐term storage. In this study, we present a framework for assessing the probability of containment effectiveness over the lifetime of a geologic carbon storage site (e.g., after 70 years of injection and postinjection site performance) using forward stochastic model realizations based on site characterization data and using a monitoring‐informed Bayesian network based on hypothetical detectability from surface seismic surveys over the site injection and post‐injection phases. The National Risk Assessment Partnership's open‐source Integrated Assessment Model (NRAP‐Open‐IAM) was utilized to develop an ensemble of 10,000 a priori stochastic forecasts of CO₂ containment. Those simulations were used to train the Bayesian network model to estimate the prior probabilities of the CO₂ leakage mass into overlying, monitorable aquifers considering the uncertainties in the reservoir properties, permeability of potentially leaky wells and the overlying aquifers. The conditional probabilities in the Bayesian network were either learned from the NRAP‐Open‐IAM simulations or derived from the predefined detection thresholds for the monitoring method. Observations obtained from monitoring, over time during the site operation phases were then used to generate updated posterior probabilities of containment (and any loss from containment) in the Bayesian network by propagating the prior probabilities through the conditional probabilities. We demonstrate how to construct and use the Bayesian network for verifying the long‐term storage complex effectiveness informed by monitoring based on the NRAP‐Open‐IAM simulations previously developed for the FutureGen 2.0 site. This approach may have relevance for stake holders to demonstrate secure geologic storage, provide a defensible, probabilistic approach to claim credit for geologic storage, and to estimate the likelihood that any fraction of the claimed credit may need to be refunded to the creditor based on available monitoring information. © 2021 Society of Chemical Industry and John Wiley & Sons, Ltd.

中文翻译：

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使用贝叶斯决策网络的综合评估建模方法评估GCS站点的遏制有效性概率

科学和工程学上对地质CO ₂行为的认识得到提高储存以及已建立的监管框架和激励机制，为加速，大规模部署这种减少温室气体排放的方法带来了前景。激励结构要求建立适当的验证和核算方法，以支持对地质存储综合体完整性的主张，并为长期存储赢得信誉。在这项研究中，我们提出了一个框架，用于评估地质碳存储站点整个生命周期内围堵有效性的可能性（例如，在进行了70年的注入和注入后性能测试之后），使用了基于站点特征数据的正向随机模型实现，并使用了基于监测的贝叶斯网络，该网络基于站点注入和注入后阶段的地表地震勘测的假设可探测性。利用国家风险评估合作伙伴关系的开源综合评估模型（NRAP-Open-IAM）来开发10,000个CO的先验随机预测集合₂ 围堵。这些模拟被用来训练贝叶斯网络模型以估计CO ₂的先验概率。 考虑到储层性质的不确定性，潜在泄漏井的渗透率和上覆含水层的不确定性，将其泄漏到上覆可监测含水层中。从NRAP-Open-IAM模拟中获悉贝叶斯网络中的条件概率，或者从监视方法的预定义检测阈值中得出贝叶斯网络中的条件概率。然后，通过在现场操作阶段的一段时间内从监视中获得的观察结果，通过在先验概率中通过条件概率进行传播，从而在贝叶斯网络中生成更新后的隐含概率（以及隐含的任何损失）。我们演示了如何构建和使用贝叶斯网络来验证基于以前针对FutureGen 2.0站点开发的NRAP-Open-IAM模拟的监视所提供的信息，从而验证长期存储复杂性的有效性。该方法可能与利益相关者具有相关性，以证明安全的地质存储，提供可辩护的概率性方法来索赔地质存储的信用，并根据可用的可用信用估计信用的任何部分可能需要退还给债权人的可能性监控信息。©2021化学工业协会和John Wiley＆Sons，Ltd. 并根据可用的监控信息来估计可能需要将部分债权退还给债权人的可能性。©2021化学工业协会和John Wiley＆Sons，Ltd. 并根据可用的监控信息来估计可能需要将部分债权退还给债权人的可能性。©2021化学工业协会和John Wiley＆Sons，Ltd.