The role of capillary forces during buoyant migration of CO2 is critical toward plume immobilization within the postinjection phase of a geological carbon sequestration operation. However, the inherent heterogeneity of the subsurface makes it very challenging to evaluate the effects of capillary forces on the storage capacity of these formations and to assess in situ plume evolution. To overcome the lack of accurate and continuous observations at the field scale and to mimic vertical migration and entrapment of realistic CO2 plumes in the presence of a background hydraulic gradient, we conducted two unique long‐term experiments in a 2.44 m × 0.5 m tank. X‐ray attenuation allowed measuring the evolution of a CO2‐surrogate fluid saturation, thus providing direct insight into capillarity‐dominated and buoyancy‐dominated flow processes occurring under successive drainage and imbibition conditions. The comparison of saturation distributions between two experimental campaigns suggests that layered‐type heterogeneity plays an important role on nonwetting phase (NWP) migration and trapping, because it leads to (i) longer displacement times (3.6 months versus 24 days) to reach stable trapping conditions, (ii) limited vertical migration of the plume (with center of mass at 39% versus 55% of aquifer thickness), and (iii) immobilization of a larger fraction of injected NWP mass (67.2% versus 51.5% of injected volume) as compared to the homogenous scenario. While these observations confirm once more the role of geological heterogeneity in controlling buoyant flows in the subsurface, they also highlight the importance of characterizing it at scales that are below seismic resolution (1–10 m).

Incorporating hysteresis into models is important to accurately capture the two phase flow behavior when porous media systems undergo cycles of drainage and imbibition such as in the cases of injection and post‐injection redistribution of CO2 during geological CO2 storage (GCS). In the traditional model of two‐phase flow, existing constitutive models that parameterize the hysteresis associated with these processes are generally based on the empirical relationships. This manuscript presents development and testing of mathematical hysteretic capillary pressure—saturation—relative permeability models with the objective of more accurately representing the redistribution of the fluids after injection. The constitutive models are developed by relating macroscopic variables to basic physics of two‐phase capillary displacements at pore‐scale and void space distribution properties. The modeling approach with the developed constitutive models with and without hysteresis as input is tested against some intermediate‐scale flow cell experiments to test the ability of the models to represent movement and capillary trapping of immiscible fluids under macroscopically homogeneous and heterogeneous conditions. The hysteretic two‐phase flow model predicted the overall plume migration and distribution during and post injection reasonably well and represented the postinjection behavior of the plume more accurately than the nonhysteretic models. Based on the results in this study, neglecting hysteresis in the constitutive models of the traditional two‐phase flow theory can seriously overpredict or underpredict the injected fluid distribution during post‐injection under both homogeneous and heterogeneous conditions, depending on the selected value of the residual saturation in the nonhysteretic models.

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One of the main concerns of geological carbon storage (GCS) systems is the risk of leakage through "weak" permeable areas of the sealing formation or caprock. Since the fluid pressure pulse travels faster than the carbon dioxide (CO 2) plume across the storage reservoir, the fluid overpressure transmitted into overlying permeable formations through caprock discontinuities is potentially detectable sooner than actual CO 2 leakage occurs. In this work, an inverse modeling method based on fluid pressure measurements collected in strata above the target CO 2 storage formation is proposed, which aims at identifying the presence, the location, and the extent of possible leakage pathways through the caprock. We combine a three-dimensional subsurface multiphase flow model with ensemble-based data assimilation algorithms to recognize potential caprock discontinuities that could undermine the long-term

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When applying environmental models, the choice of model complexity and the design of field campaigns depend on each other and on the modeling/prediction goal. We propose jointly optimizing model complexity and data collection (design) by maximizing the expected performance for the modeling goal. We use ensembles of highly resolved virtual realities and of less complex modeling variants that differ in their degrees of upscaling and simplified parameterization. For each design under consideration, we simulate hypothetical measurement data (subject to noise) with all realizations of all models. To mimic model calibration with hypothetical data, we identify pairs of best fitting realizations between virtual reality and each model variant for each design. Then, we emulate model choice by selecting (across the model variants, for each design and for each virtual reality) the pair that shows the best predictive match in the modeling goal. Finally, we identify the model/design combination that offers, on average over all virtual realities, the best predictive match. As a test application, we consider a heterogeneous, stratified aquifer, in which the stratification enhances hydraulic anisotropy on the macroscale. We define two different modeling goals: (a) estimating the hydraulic conductivity tensor upscaled to the full aquifer thickness and (b) predicting the pumping rate needed to dewater a construction pit. Our results indicate that jointly optimizing observation networks and model selection can reduce the prediction uncertainty of parameters at lower experimental costs. We also show that the involved trade‐offs between model complexity and required design depend on the target quantity.

Geological carbon storage (GCS) has been proposed as a favorable technology to reduce carbon dioxide (CO 2 ) emissions to the atmosphere. One of the main concerns about GCS is the risk of CO 2 escape from the storage formation through leakage pathways in the sealing layer. This study aims at understanding the main sources of uncertainty affecting the upward migration of CO 2 through preexisting "passive" wells and the risk of fissuring of target formation during GCS operations, which may create pathways for CO 2 escape. The analysis focuses on a potential GCS site located within the Michigan Basin, a geologic basin situated on the Lower Peninsula of the state of Michigan. For this purpose, we perform a stochastic analysis (SA) and a global sensitivity analysis (GSA) to investigate the influence of uncertain parameters such as: permeability and porosity of the injection formation, passive well permeability, system compressibility, brine residual saturation and CO 2 end-point relative permeability.For the GSA, we apply the extended Fourier Amplitude Sensitivity Test (FAST), which can rank parameters based on their direct impact on the output, or first-order effect, and capture the interaction effect of one parameter with the others, or higher-order effect. To simulate GCS, we use an efficient semianalytical multiphase flow model, which makes the application of the SA and the GSA computationally affordable. Results show that, among model parameters, the most influential on both fluid overpressure and CO 2 mass leakage is the injection formation permeability. Brine residual saturation also has a significant impact on fluid overpressure. While influence of permeability on fluid overpressure is mostly first-order, brine residual saturation's influence is mostly higher-order. CO 2 mass leakage is also affected by passive well permeability, followed by porosity and system compressibility through higher order effects.

The impact of geologic heterogeneity on capillary trapping of supercritical CO2 (scCO2) has been recognized and appraised through laboratory experimentation and modeling. However, how different injection strategies can be optimized to improve capillary trapping has not received adequate attention. We present a study based on stochastic analysis to show the impact of injection scheduling on capillary trapping of scCO2 in heterogeneous geological formations. Improvement of trapping efficiency consists of maximizing the trapped volume within a predefined secure zone of the storage formation with the goal of avoiding plume intersection with potential leakage pathways. Using knowledge acquired from physical experiments in intermediate‐scale tanks with controlled heterogeneity, we conduct numerical simulations following a stochastic approach to extend the observed outcomes to a series of equally probable permeability scenarios. Our simulations involve the same combination of surrogate fluids that are used in the experiments to mimic viscosity and density contrasts between scCO2 and brine. To account for uncertainty of formation heterogeneity, several permeability fields with three different variances and two horizontal correlation lengths are generated. The same volume of scCO2 is emplaced using four different modes of injection scheduling. Results suggest that injection strategies aimed at enhancing capillary trapping of scCO2 and increasing the probability of constraining the plume within a secure zone are strictly related to the specific heterogeneity of the reservoir. This preliminary theoretical finding suggests the potential for maximizing secure capillary trapping through the proper selection of injection schedule to fit the formation heterogeneity. © 2016 Society of Chemical Industry and John Wiley & Sons, Ltd.