Surfactant flooding has become a more common method in recent decades in which the phase behavior inside the reservoir can be manipulated by the injection of surfactants, leading to favorable conditions to mobilize trapped oil. Carefully designed surfactants that are injected into the crude oil can generate microemulsions at the interface between crude oil and water, thereby reducing the interfacial tension to very low levels, which ultimately mobilizes the residual oil. However, the adsorption of surfactant to the rock during the injection and chromatographic separation of the surfactant in the reservoir are critical phenomena in which the surfactants must be resistant to reservoir conditions, including high pressures and temperatures. The application of surfactant methods is usually constrained by the cost of the chemicals and is adversely affected by adsorption and loss into the rock, making it crucial to understand the significance of those factors that affect the performance of this enhanced oil-recovery method so that more robust decisions can be made. This study aims to outline the lessons learned regarding the impact that these factors had on a North Sea reservoir.
In this study, a reservoir simulation model is harnessed in which the injection of cold water into the initially hot reservoir results in a reverse temperature gradient is simulated using a commercial full-physics reservoir simulator. A surfactant slug is followed by several years of water injection, where the reservoir contains a light oil trapped between a gas cap and an aquifer.
Surfactant effects on residual-oil detrapping and wettability are modeled by interpolating between relative permeability sets according to a capillary number, which depends on temperature and surfactant concentration, where composition-dependent K-values describe multiple-contact water-oil miscibility according to the concentration of water-based surfactant in which the adsorption of surfactant affects its availability and propagation.
Using a real reservoir model coupled with a robust optimization tool to provide useful insights beyond the limitations of the theoretical results, this study takes an extra step beyond experimental data and simulation studies that are carried out with synthetic models. The influence of each decision and uncertainty parameter important in reservoir management decisions is outlined with the optimization results under different scenarios on the performance of surfactant flood, thereby illustrating the phenomenon with a real reservoir model that will shed light onto similar cases.