Optimization of ionic concentrations in engineered water injection in carbonate reservoir through ANN and FGA

Abstract

Engineered Water Injection (EWI) has been increasingly tested and applied to enhance fluid displacement in reservoirs. The modification of ionic concentration provides interactions with the pore wall, which facilitates the oil mobility. This mechanism in carbonates alters the natural rock wettability being quite an attractive recovery method. Currently, numerical simulation with this injection method remains limited to simplified models based on experimental data. Therefore, this study uses Artificial Neural Networks (ANN) learnability to incorporate the analytical correlation between the ionic combination and the relative permeability (Kr), which depicts the wettability alteration. The ionic composition in the injection system of a Brazilian Pre-Salt benchmark is optimized to maximize the Net Present Value (NPV) of the field. The optimization results indicate the EWI to be the most profitable method for the cases tested. EWI also increased oil recovery by about 8.7% with the same injected amount and reduced the accumulated water production around 52%, compared to the common water injection.