We study the influence of different physical properties on the effectiveness of CO 2 storage in aquifers. We present the results of a numerical sensitivity analysis using experimental design to quantify and compare the contribution of the most important parameters to the trapping of CO 2 .The work focuses on the impact of dissolution and residual trapping. Simulations using a reservoir model with properties and geometries representative of the Stuggart Formation in Ketzin (Germany) demonstrate how different trapping mechanisms are influenced by gravity segregation, fingering, and channeling.These studies show that horizontal permeability is the most influential parameter on the total amount of CO 2 dissolved, as it facilitates the lateral migration of CO 2 , enhancing dissolution into the brine. Residual gas saturation S gr is found to be the greatest contributor to the amount of residual CO 2 . As expected, higher S gr will reduce CO 2 mobility, producing a higher residual trail left by the CO 2 plume as it migrates. In addition, permeability heterogeneity is a major contributor to both trapping mechanisms.After a suitable geological site for CO 2 storage has been selected, our studies suggest that with appropriate well placement and injection strategy, at least 80% of the stored CO 2 can be trapped within a few decades of the end of injection.

Significant challenges remain in the development of optimized control techniques for intelligent wells, particularly with respect to properly incorporating the impact of reservoir uncertainty. Most optimization methods are model-based and are effective only if the model can be used to predict future reservoir behavior with no uncertainty. Recently developed schemes, which update models with data acquired during the optimization process, are computationally very expensive. We suggest that simple reactive control techniques, triggered by permanently installed downhole sensors, can enhance production and mitigate reservoir uncertainty across a range of production scenarios. We assess the implementation of an intelligent horizontal well in a thin oil rim reservoir in the presence of reservoir uncertainty, and evaluate the benefit of using two completions in conjunction with surface and downhole monitoring. Three control strategies are tested. The first is a simple, passive approach using a fixed control device to balance inflow along the well, sized prior to installation. The second and third control strategies are reactive, employing intelligent completions that can be controlled from the surface. The second strategy opens or closes the completions according to well water cut and flow rate and individual downhole rate and phase measurements obtained from a surface multiphase flowmeter and alternating zonal well tests. The third strategy proportionally chokes the completions as increased completion water cut is measured using downhole multiphase flowmeters. A cost-benefit analysis demonstrates that reactive control strategies always yield a neutral or positive return, whereas a passive, model-based strategy can yield negative returns if the reservoir behavior is poorly understood. While reactive control strategies enhance production and mitigate reservoir uncertainty, they may not deliver the optimum possible solution. Proactive control techniques, which additionally incorporate data from downhole reservoir-imaging sensors, may yield nearoptimal gains.

This paper describes a simulation study of the low-salinity effect in sandstone reservoirs. The proposed mechanistic model allows differentiation of water composition effects and includes multi-ionic exchange and double layer expansion. The manifestation of these effects can be observed in coreflood experiments. We define a set of chemical reactions, to describe the contribution of van der Waals forces, ligand exchange, and cation bridging to mobilization of residual oil. The reaction set is simplified by incorporating wettability weighting coefficients that reflect the contribution of different adsorbed ions to the wettability of the rock. Changes in wettability are accounted for by interpolation of the relative permeability and capillary pressure curves between the low and high salinity sets. We also construct and test simplified phenomenological models, one relating the change of the relative permeability to the concentration of a dissolved salinity tracer and another one to the concentration of a single adsorbed tracer. The full mechanistic model, with multiple ion tracking, is in good qualitative agreement with experimental data reported in the literature. A very close agreement with the mechanistic model was obtained for a coreflood simulation using single tracer phenomenological models. The similarity of the results is explained by the fact that the most critical factor influencing the flow behavior was the function used to interpolate between the oil- and water-wet sets of saturation curves. Similar interpolation functions in different models lead to similar oil recovery predictions. This study has developed a detailed chemical reaction model that captures both multicomponent ion exchange and double layer expansion effects, and can be used to improve understanding of low-salinity recovery mechanisms by analyzing their relative contributions. The approach of matching a tracer model to a detailed mechanistic model promises a route to the development of simplified, less computationally demanding proxy models for full field simulation studies.

A dual-porosity simulation model is a coarse upscaled representation of a naturally fractured reservoir. Fluid transfer between the matrix and the fracture is described by a matrix-fracture transfer function, which is dependent on a shape factor. However, the basic formulation assumes pseudosteady-state conditions and requires modifications to capture transient effects. This paper describes the use of a dynamic shape factor for matrix-fracture transmissibility with block-to-block effects to improve the simulation of oil recovery in a dual-porosity model. A simple fine-grid single-porosity model is compared with a coarse-grid dual-porosity equivalent for a gas-oil system under gravity drainage without capillary effects. A time-varying relationship between the shape factor and the matrix oil saturation is derived by numerical analysis. Vertical block-to-block connections are included in the model to match oil reinfiltration from the fractures to the matrix. The saturation-dependent shape factor correlation is generalized for other matrix block sizes. An improved match to fine-grid recovery is achieved in the dual-porosity simulation through use of a dynamic transfer function and block-to-block effects. The methodology is shown to be appropriate for a range of matrix sizes and, with different relative permeability curves, for the matrix blocks. However, attention must be paid to the relationship between simulation grid cell size and geologic matrix block size. Additional study of block-to-block flows is recommended to further improve the predictive capability of the model. Fast and accurate simulation of flow in naturally fractured reservoirs is often difficult to achieve. The standard matrix-fracture transfer model is unsuitable for many situations. In the example presented here, we show how a coarse simulation model can be used effectively to capture variations in dynamic matrix-fracture transfer behavior over time for a specific case. The approach can be generalized and applied to similar studies.

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