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

Abstract

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. The model, trained with data from a pilot-scale dynamic simulator, achieved high predictive accuracy, featuring a regression value of 0.996 and a mean square error of 0.0011. It effectively forecasts key performance indicators such as oil recovery, water cut, and well bottom-hole pressure. The model identified injection rate as the most influential factor and polymer concentration as the least significant. Through the optimization of input parameters, the study established optimized values for the injection rate, injected fluid salinity, and polymer concentration at 1450 (bbl/day), 4000 ppm, and 1500 ppm, respectively. The optimization considers economic viability by maximizing net present value and addresses practical challenges of maintaining injectivity over time, making it a valuable tool for enhancing water-based recovery methods in oil field development.