A Universal Method for Predicting the Relative Permeability Data of Low Salinity Injection

Abstract

Abstract Low salinity waterflood (LSWF) injection is an enhanced oil recovery (EOR) method proven effective through extensive experimental studies. Correct implementation of this method in reservoir-scale simulations requires reliable estimation of changes in relative permeability data associated with LSWF. For this purpose, a few models have been suggested based on geochemical interactions, such as the cation exchange capacity of clay, which are case dependent and cannot be applied to all systems. This study presents a novel semi-empirical model based on incremental oil recovery measured during low salinity injection. Therefore, it can be applied to all rock types, fluid systems, and wettability conditions regardless of the active mechanism. Some mechanisms proposed in the literature relate the additional oil recovery during low salinity injection to measurable parameters such as micro-dispersion. As a result, the kr curves can be constructed using this new methodology by measuring the micro-dispersion. This method has been validated against five sets of secondary and tertiary coreflood experiments published in the literature. First, the high salinity kr data is obtained by history matching using the CMOST module of CMG software. Then the proposed method and the measured value of additional oil recovery were used to estimate the kr data of low salinity injection. The results showed that the suggested method could predict the oil recovery and pressure drop in secondary and tertiary modes. The high-salinity relative permeability was shifted towards a more water-wet condition in tertiary mode. The kr curve of secondary LSWF showed a significant shift towards a more water-wet condition than tertiary mode, implying lower residual oil saturation. Since the additional oil recovery versus micro-dispersion curve was reported for this rock sample, one can simply predict the kr values of LSWF for other values of micro-dispersion. Due to the ongoing debate regarding the dominant mechanism during LSWF, there is no universal model for estimating the relative permeability of LSWF in all systems. The model presented in this paper provides a powerful tool for engineers to simulate the LSWF kr data in both tertiary and secondary flooding regardless of the active mechanism.