Bi-directional risk assessment in carbon capture and storage with Bayesian Networks. (April 2015)

• Record Type: Journal Article
• Authors: Gerstenberger, M.C.; Christophersen, A.; Buxton, R.; Nicol, A.
• Is Part Of: International journal of greenhouse gas control. Volume 35(2015:Apr.)
• Page Start: 150
• Page End: 159
• Publication Date: 2015-04
• Subjects: CCS -- Bayesian Networks -- Risk assessment; Greenhouse gases -- Environmental aspects -- Periodicals; Air -- Purification -- Technological innovations -- Periodicals; Gaz à effet de serre -- Périodiques; Gaz à effet de serre -- Réduction -- Périodiques; Air -- Purification -- Technological innovations; Greenhouse gases -- Environmental aspects; Periodicals; 363.73874605
• DOI: 10.1016/j.ijggc.2015.01.010
• Languages: English
• ISSNs: 1750-5836

Abstract

Abstract : Highlights: Development of a Bayesian Network for risk assessment in CCS. Application of a simple Bayesian Network in CCS risk assessment. Expert elicitation and uncertainty in CCS risk assessment. Abstract: The complex system required for carbon capture and storage (CCS) encompasses numerous sub-systems with inter-dependencies and large parameter uncertainties that propagate throughout the system. Exploring and understanding these inter-dependencies and uncertainties is invaluable for developing robust risk information. Bayesian Networks (BN), a decision support tool, are being increasingly used in the broader risk assessment community and show promise for use in CCS. BNs explore the dependencies and uncertainties within a system and have the potential to provide a better understanding of risk than more traditional tools such as logic trees or other less integrated approaches. Working with experts from within the Cooperative Research Centre for Greenhouse Gas Technologies (CO2CRC), we have developed a generic BN structure for the storage sub-system of CCS which can be used to guide the development of BNs for other CCS applications and for use in both diagnostic and predictive analysis. This bi-directionality provides one of the more important benefits of BNs; it allows for (1) traditional bottom-up risk assessment where the likely consequences based on the expected state of the system can be calculated and also (2) top-down, or outcome oriented risk, where the state of the system leading to a particular outcome, such as the likelihood of 2% leakage in 1000 years, is determined. This allows for a comprehensive sensitivity analysis which highlights important contributors to the risk and also where additional knowledge may benefit the project and reduce uncertainty. A robust expert elicitation procedure, for both the development of the network structure and the determination of event probabilities, is an integral part of the use of any such BN tool in CCS. Finally, we show the direct application of a smaller CCS BN by the CO2CRC.