Evaluating probability of containment effectiveness at a GCS site using integrated assessment modeling approach with Bayesian decision network. Issue 2 (13th March 2021)

• Record Type: Journal Article
• Authors: Wang, Zan; Dilmore, Robert M.; Bacon, Diana H.; Harbert, William
• Is Part Of: Greenhouse gases. Volume 11:Issue 2(2021)
• Page Start: 360
• Page End: 376
• Publication Date: 2021-03-13
• Subjects: Bayesian network -- carbon sequestration -- monitoring -- NRAP‐Open‐IAM -- risk assessment; Greenhouse gases -- Periodicals; Greenhouse gas mitigation -- Periodicals; 363.7387405
• DOI: 10.1002/ghg.2056
• Languages: English
• ISSNs: 2152-3878

Abstract

Abstract: 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.