Managing Uncertainty in Geological CO2 Storage Using Bayesian Evidential Learning
نویسندگان
چکیده
Carbon capture and storage (CCS) has been increasingly looking like a promising strategy to reduce CO2 emissions meet the Paris agreement’s climate target. To ensure that CCS is safe successful, an efficient monitoring program will prevent reservoir leakage drinking water contamination in groundwater aquifers must be implemented. However, geologic sequestration (GCS) sites are not completely certain about geological properties, which makes it difficult predict behavior of injected gases, brine rates through wellbores, plume migration. Significant effort required observe how behaves reservoirs. A key question is: Will injection behave as expected, can we anticipate leakages? History matching models mitigate uncertainty towards predictive strategy. It could prove challenging develop set history preserve realism. new Bayesian evidential learning (BEL) protocol for quantification was released literature, alternative model-space inversion history-matching approach. Consequently, ensemble previous developed using prior distribution’s Monte Carlo simulation, followed by direct forecasting (DF) joint quantification. The goal this work use identify statistical relationship between data prediction, models, variables, without any explicit model inversion. paper also introduces DF implementation smoother shows make computation more robust than standard method. Utsira saline aquifer west Norway used exemplify BEL’s ability mass leakages improve decision support regarding projects.
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ژورنال
عنوان ژورنال: Energies
سال: 2021
ISSN: ['1996-1073']
DOI: https://doi.org/10.3390/en14061557