A potential risk of injecting CO2 into storage reservoirs with marginal permeability (≲ 10-14 m2) is that commercial injection rates could induce fracturing of the reservoir and/or the caprock. Such fracturing is essentially fluid-driven fracturing in the leakoff-dominated regime. Recent studies suggested that fracturing, if contained within the lower portion of the caprock complex, could substantially improve the injectivity without compromising the overall seal integrity. Modeling this phenomenon entails complex coupled interactions among the fluids, the fracture, the reservoir, and the caprock. We develop a simple method to capture all these interplays in high fidelity by sequentially coupling a hydraulic fracturing module with a coupled thermal-hydrological-mechanical (THM) model for nonisothermal multiphase flow. The model was made numerically tractable by taking advantage of self-stabilizing features of leakoff-dominated fracturing. The model is validated against the PKN solution in the leakoff-dominated regime. Moreover, we employ the model to study thermo-poromechanical responses of a fluid-driven fracture in a field-scale carbon storage reservoir that is loosely based on the In Salah project’s Krechba reservoir. The model reveals complex yet intriguing behaviors of the reservoir-caprock-fluid system with fracturing induced by cold CO2 injection. We also study the effects of the in situ stress contrast between the reservoir and caprock and thermal contraction on the vertical containment of the fracture. The proposed model proves effective in simulating practical problems on length and time scales relevant to geological carbon storage.

Seismic sensors and seismic imaging have been widely used to monitor the geophysical properties of the subsurface. As subsurface engineering techniques advance, more precise monitoring systems are required. Seismic event catalogs and seismic velocity structures are two of the major outputs of seismic monitoring systems. Although seismic event catalogs and velocity structure are often studied separately, published reports suggest constraining them simultaneously can lead to better results. We conducted a double-difference seismic tomography analysis to constrain both the seismic event locations and the 3D seismic velocity structure. Passive seismic data collected from a geothermal research project in Lead, South Dakota were used to image a 3D volume on the scale of tens of meters. Specifically, around 18,500 P-wave and 8,900 S-wave arrival times from 1,874 seismic events were used. Checkerboard tests showed that the observed data can image the seismically active region well. We compared tomography results with fixed seismic event locations against those with updated event locations. Tomography results with updated event locations showed better fits to the observations and improved the seismic event catalog, showing sharper patterns compared to the original one. These patterns helped us monitor the seismically active fractures since the seismic events were mostly due to hydraulic stimulations. Two parallel fractures revealed by the updated seismic event catalog spatially correlated with independent borehole temperature observations. The average seismic velocity values of the well-constrained volume agreed to the first order with core sample measurements and active-source seismic surveys.

Predicting the thermal performance of an enhanced geothermal system (EGS) requires a comprehensive characterization of the underlying fracture flow patterns from practically available data such as tracer data. However, due to the inherent complexities of subsurface fractures and the generally insufficient geological/geophysical data, interpreting tracer data for fracture flow characterization and thermal prediction remains a challenging task. The present study aims to tackle the challenge by leveraging a data assimilation method to maximize the utilization of information inherently contained in tracer data, and meanwhile maintain the flexibility to handle various uncertainties. A tracer data interpretation framework was proposed with the following three components integrated: 1) We use principal component analysis (PCA) to reduce the dimensionality of model parameter space. 2) We use ES-MDA (ensemble smoother with multiple data assimilation) to invert for fracture aperture/flow fields and obtain posterior model ensembles for uncertainty quantification. Various data types are assimilated jointly to improve the predictive ability of the posterior ensemble. 3) The inverted fracture aperture fields are then incorporated into reservoir models to predict thermal performance. We developed a field-scale EGS model to verify the ability of the framework to characterize highly heterogeneous fracture aperture/flow fields and predicting thermal performance. We also applied the framework to a meso-scale field experiment to demonstrate its potential application in real-world geothermal reservoirs. The results indicate that the proposed framework can effectively retrieve fracture flow information from tracer data for thermal prediction and uncertainty quantification, and thus provide informative guidance for EGS optimization and risk management.