Multi-Scale Joint Deformation–BHP Data Assimilation for CO2 Storage Reservoir Characterization Using ES-MDA

초록

Reliable prediction of coupled hydraulic–geomechanical behavior is essential for safe, long-term geological carbon storage (GCS). However, limited and spatially sparse observations from injection sites create large uncertainties in reservoir properties, reducing the reliability of CO₂ migration forecasts and monitoring, measurement, and verification programs. We present an integrated thermal–hydrological–mechanical data-assimilation framework using the Ensemble Smoother with Multiple Data Assimilation (ES-MDA) to jointly assimilate multi-physics and multi-scale observations. The framework combines time-series bottom-hole pressure (BHP) and spatial surface deformation data to constrain key hydraulic and geomechanical parameters in a fully coupled manner. We demonstrate the workflow in a 3-D channelized anticlinal aquifer model after performing sensitivity analysis, revealing updates to key reservoir parameters, including (1) hydraulic, (2) geomechanical, (3) wellbore design, and (4) reservoir/aquifer properties. BHP-only assimilation constrains permeability, well-skin factors, and sandstone relative-permeability parameters, whereas joint BHP–deformation assimilation further sharpens these flow-related estimates and improves the estimation of aquifer thickness and elastic properties, notably enhancing the centering of posterior Young’s moduli in sandstone and shaly sandstone. Joint assimilation also yields narrower posterior ensembles and lower mean squared errors during both the three-year assimilation period and the subsequent two-year forecast, enabling more reliable predictions of future reservoir pressure and surface deformation. This study shows that critical reservoir properties, including saturation-dependent relative-permeability functions and the Young’s modulus of the sandstone layer, can be robustly quantified by integrating both BHP and deformation data. The proposed multi-scale ES-MDA framework reduces predictive uncertainty, improves reservoir characterization, and supports secure, large-scale deployment of GCS. © 1980-2012 IEEE.

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