Evaluating probability of containment effectiveness at a GCS site using integrated assessment modeling approach with Bayesian decision network

Abstract

Improved scientific and engineering understanding of the behavior of geologic CO 2 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 CO 2 containment. Those simulations were used to train the Bayesian network model to estimate the prior probabilities of the CO 2 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

A quantitative risk assessment approach for developing contingency plans at a geologic carbon storage site

“…[7][8][9] Integrated system modeling approaches link sophisticated models of each process at the site (such as storage reservoir dynamics, leakage pathway behavior, and groundwater aquifer impact from leakage) to provide credible forecasts of system behavior that can be used to make key decisions during the development and operation of a GCS site. [52][53][54][55][56] These modeling approaches have been used to evaluate leakage risks, 19,23,[57][58][59][60][61][62][63] inform and optimize both leakage monitoring networks and the duration of postinjection site care, 23,[64][65][66][67][68] assess the long-term probability of successful CO 2 containment, 69 and determine the AoR for a project. 57,70,71 System-based risk assessment modeling can also aid in the development of contingency plans for a GCS operation by quantitatively predicting the impact of various response actions considered on leakage risks amidst uncertainty, but such modeling has not yet been applied in this context.…”