Modeling alterations in relative permeability curves due to salinity using artificial neural networks

We propose data-driven models based on artificial neural networks (ANN) to predict changes in water-oil relative permeability curves given a salinity reduction in the injection water. The ANN consisted of a multilayer feedforward structure with backpropagation. For validation, a database from a semi-empirical correlation was created, and models with added noise were used to analyze the influence of the data dispersion. Then, a survey of experimental relative permeability curves was performed to produce a real database for sandstone and carbonate rocks, utilized in the training of the final models, with hyperparameter optimization and cross-validation. The initial model was able to consistently reproduce the original correlation, with a mean squared error (MSE) on the order of \(10^{-6}\) ${10}^{-6}$ . In the noise-trained model, the error measured was lower than the analytical error expected from random dispersion. In models trained with real data, the adopted strategy led to a final training MSE on the order of \(10^{-3}\) ${10}^{-3}$ , with better performance in networks with two hidden layers. The obtained models are useful in modeling relative permeabilities for low-salinity and engineered water injection projects. Training can be continuously updated with new data, and the methodology can be applied to other properties or even other multivariate regression problems.