Uncertainty quantification under data worth for the Delft campus geothermal project

Abstract

Low-enthalpy geothermal doublets for direct-use heating are highly sensitive to subsurface heterogeneity and operational uncertainty. This study quantifies these uncertainties for the Delft campus geothermal system using an integrated workflow that couples geological modeling with GPU-accelerated, high-spatial-resolution reservoir simulation. Ensembles of three-dimensional regional-scale facies models of the Delft Sandstone Member, with and without conditioning to Geothermie Delft (GTD) well data, were generated using object-based modeling and sequential indicator simulation. Porosity was modeled by sequential Gaussian simulation, and permeability was derived from a nuclear magnetic resonance-based porosity–permeability correlation calibrated to GTD well data, yielding higher permeability than core-based correlations for porosity below 15%. Lorenz coefficients indicate strong variability in property distributions, resulting in a wide spread of production temperatures. In total, 2000 geological realizations were simulated over 50 years using the GPU-enabled open-source Delft Advanced Research Terra Simulator (open-DARTS) under maximum and 2025-demand production schemes. Conditioning to GTD wells adds considerable data worth by constraining uncertainty in production well bottom-hole temperature (BHT) and pressure (BHP), while keeping injection pressure within Dutch regulatory limits. SIS models exhibit greater temperature and pressure variability than OBM models due to lower sand-body continuity. Despite large 80% confidence intervals, P50 production temperatures remain comparable for conditioned models. Distance-based generalized sensitivity analysis identifies net-to-gross ratio and the porosity–permeability correlation as dominant controls on thermal response. The 2025-demand scheme delays cold-front propagation. Results demonstrate that ensemble-based, GPU-accelerated high-spatial-resolution simulations enable robust and efficient uncertainty quantification for direct-use geothermal systems, highlighting the importance of well conditioning and reservoir heterogeneity characterization in constraining thermal responses.