We propose a quantitative model-based workflow for conformance verification of CO2 storage projects. Bayesian inference is applied to update an ensemble of simulation models that capture prior uncertainty based on mismatches with measured data. Conformance assessments are derived
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We propose a quantitative model-based workflow for conformance verification of CO2 storage projects. Bayesian inference is applied to update an ensemble of simulation models that capture prior uncertainty based on mismatches with measured data. Conformance assessments are derived by comparison of updated model predictions with storage permit requirements and confidence criteria. Two examples, one conceptual and one based on a real candidate storage site, are provided in which the quantitative workflow is applied to the a priori assessment of candidate monitoring strategies. The examples illustrate the limitations of pressure monitoring in the presence of realistic subsurface uncertainties, and the potential for cost saving by informed design of geophysical monitoring surveys. Approximate methods are discussed that could make the workflow also applicable for (quasi) real-time conformance monitoring.@en
