Hybrid Machine Learning for Modeling the Relative Permeability Changes in Carbonate Reservoirs under Engineered Water Injection

Advanced production methods utilize complex fluid iteration mechanisms to provide benefits in their implementation. However, modeling these effects with efficiency or accuracy is always a challenge. Machine Learning (ML) applications, which are fundamentally data-driven, can play crucial role this context. Therefore, study, we applied Hybrid (HML) solution predict petrophysical behaviors during Engineered Water Injection (EWI). This hybrid approach utilizes K-Means and Artificial Neural Network algorithms EWI. In addition, an optimization process maximize the Net Present Value (NPV) of case results demonstrate that HML outperforms conventional by increasing oil (7.3%) while decreasing amount water injected produced (by 28% 40%, respectively). Even when injection price higher, method remains profitable. our study highlights potential utilizing solutions for predicting significantly improve advanced methods, may help profitability new mature fields.