A new insight into pilot-scale development of low-salinity polymer flood using an intelligent-based proxy model coupled with particle swarm optimization.

To successfully implement low-salinity polymer flooding in heterogeneous heavy oil reservoirs, it is crucial to comprehend the interactions between salinity, polymer properties, and reservoir characteristics. Artificial intelligence-driven proxy models can assist in identifying critical parameters and predicting performance outcomes, thereby enabling optimizing field-scale applications of this technique in heterogeneous heavy oil reservoirs. This study focused on developing a proxy model by coupling neural network and particle swarm optimization algorithms to analyze low-salinity polymer flooding.

Low salinity water flooding: estimating relative permeability and capillary pressure using coupling of particle swarm optimization and machine learning technique.

The reservoir's properties are required for proper reservoir simulation, which also involves uncertainties. Experimental methods to estimate the relative permeability and capillary pressure data are expensive and time-consuming. This study aims to determine the relative permeability and capillary pressure functions of a sandstone core in the presence and absence of clay during low-salinity water floods.