Determining Performance Indicators for Linking Monitoring Results and Risk Assessment – Application to the CO2 Storage Pilot of Hontomin, Spain
14 Pages Posted: 16 Apr 2019 Last revised: 27 Oct 2020
Date Written: September 4, 2018
Risk management is an essential part of any industrial operation, and relevant for CO2 injection and storage. Risk management is essential not only to ensure that there will be no detrimental impacts to public health or the environment, but also as a means to building trust in stakeholders. Operational risk management can be divided into three parts, namely: 1) risk assessment, where the risk is studied (this phase commonly involves numerical modelling); 2) monitoring during operations in order to check that the evolution of the site is in line with the pre-activity assessment; and 3) risk mitigation or risk treatment which includes any measure or action that can lower the risk either before or during operations. In the case of a CO2 storage site, risk management activities also apply to the post-closure phase, until transfer of liability.
The goal of the ENOS project on this matter is to propose a method for a robust, integrated risk management system. More in particular, we focus on a method for determining indicators and thresholds for linking monitoring and risk assessment. The idea is to expand to the other risks encountered in the analysed activity, the principle of traffic-light systems used for mitigating induced seismicity. One of the main challenge is to build a method that can take account of various measurements, and of specific purposes of different monitoring approaches. In particular, the monitoring results will be used to check that both performance and concordance (i.e., agreement between measurements and models) are acceptable. The proposed method is based on Bayesian framework and consists in the design of several models, each for the potential behaviour of a specific measurement.
The models should reflect many potential scenarios, including an expected evolution of the corresponding measurement, the identified risk scenarios, as well as other, unexpected scenarios. The Bayesian framework allows to update the probability of each model with the acquisition of experimental data, and thresholds can then be set as probabilities. In this paper, the first steps of the method in ENOS are demonstrated for the case of the Hontomìn CO2 injection and storage pilot site in Spain.
Keywords: CO2 storage, risk management, Bayesian framework, thresholds, monitoring
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