Hybrid Particle Swarm Optimization Coupled by Machine Learning Technique for Estimating Relative Permeability and Capillary Pressure Functions During Low-Salinity Flood

Experimental methods to estimate the relative permeability and capillary pressure data are expensive and time-consuming, thus evolutionary and stochastic algorithms to estimate these parameters could be a promising solution. This study aims to determine the relative permeability and capillary pressure functions of a heterogeneous sandstone core in the presence and absence of clay during low-salinity water flood. The relative permeability and capillary pressure data were determined by automatic history matching recovery factor and pressure drop results from previously lab-reported data, through coupling a simulator with the particle swarm optimization algorithm. A series of correlations were proposed based on multiple-linear regression for relative permeability parameters at the desired low-salinity conditions. They were validated against experimental results of no clay and clayey formation with regression of 95% and 97%, and MSE of 1.2E-04 and 8E-05. One single curve for relative permeability and capillary pressure obtained from averaging methods was assigned to the grid cells for core scale low-salinity flood simulation. Accordingly, the effect of salinity and clay content on the obtained curves was investigated. The proposed hybrid method could be a suitable tool to estimate relative permeability and capillary pressure functions at the core and field level of the water-based EOR methods.