Md
M.M. de Nooijer
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Optimizing Uncertainty Quantification for CO2 Subsurface Storage
Model Ranking Using Flow Diagnostics
This thesis develops a geological ensemble-reduction workflow for efficient uncertainty quantification (UQ) in Carbon Capture and Storage (CCS). Using early-time full-physics (FP) injection-rate data and distance-based clustering, representative models are selected to preserve ensemble percentile bounds (P10–P50-P90) within ≤5% relative RMSE. Applied to two 108-member ensembles, injection-rate uncertainty distributions were accurately reconstructed using only 6 and 9 representative models (∼7–10% of the total simulation cost). Early FP rates (≤10 days) proved strongly predictive of long-term behavior and key geological controls. Distance-based generalized sensitivity analysis (dGSA) effectively identified influential parameters governing reservoir response variability at only ~1.3% of the full simulation cost. The approach provides a transparent, low-cost framework for CCS reservoir UQ, enabling robust risk and performance assessment with order-of-magnitude computational savings.
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This thesis develops a geological ensemble-reduction workflow for efficient uncertainty quantification (UQ) in Carbon Capture and Storage (CCS). Using early-time full-physics (FP) injection-rate data and distance-based clustering, representative models are selected to preserve ensemble percentile bounds (P10–P50-P90) within ≤5% relative RMSE. Applied to two 108-member ensembles, injection-rate uncertainty distributions were accurately reconstructed using only 6 and 9 representative models (∼7–10% of the total simulation cost). Early FP rates (≤10 days) proved strongly predictive of long-term behavior and key geological controls. Distance-based generalized sensitivity analysis (dGSA) effectively identified influential parameters governing reservoir response variability at only ~1.3% of the full simulation cost. The approach provides a transparent, low-cost framework for CCS reservoir UQ, enabling robust risk and performance assessment with order-of-magnitude computational savings.