A Bayesian network and structured expert elicitation for Otway Stage 2C: Detection of injected CO2 in a saline aquifer. (August 2016)

• Record Type: Journal Article
• Authors: Gerstenberger, M.C.; Christophersen, A.
• Is Part Of: International journal of greenhouse gas control. Volume 51(2016:Aug.)
• Page Start: 317
• Page End: 329
• Publication Date: 2016-08
• Subjects: CCS -- Risk assessment -- Bayesian network -- BN -- BBN -- Expert elicitation -- Classical Model -- Calibration questions; 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.2016.05.011
• Languages: English
• ISSNs: 1750-5836

Abstract

Abstract : Highlights: Development of a Bayesian belief network for risk assessment in CCS. Structured expert elicitation with the Classical Model. Uncertainty analysis in expert elicitation and risk assessment. Abstract: This paper describes, to our knowledge, the first application of a Bayesian network (BN) with structured expert elicitation in risk assessment of carbon storage. The BN estimates the probability that the CO2CRC Otway Stage 2C experiment is successful in detecting a CO2 plume injected into a saline aquifer and in confirming stabilisation of the plume using a 4D seismic survey. To develop the BN structure, Otway Stage 2C scientists and managers identified the key variables impacting the success of the experiment and their relations. To estimate the conditional probabilities required to quantify the BN, we applied the Classical Model. This is a non-consensus procedure for combining expert judgement that involves weighting a group of experts according to their ability to judge uncertainties accurately and informatively. The experts participated in open discussion about the BN and provided, individually, all probabilities required to complete the BN. The primary result was a 74% probability of detecting the plume, and a 57% probability that there will be consistency between the model-predicted plume behaviour and the observations.