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

• Published in: Computational geosciences 2024-12, Vol.28 (6), p.1115-1129
• Main Authors: Czarnobay, Vinicius, Lamas, Luis Fernando, Sebrão, Damianni, Hegele, Luiz Adolfo
• Format: Article
• Language: English
• Subjects: Analytical errors; Artificial neural networks; Back propagation networks; Carbonate rocks; Carbonates; Data analysis; Dispersion; Earth and Environmental Science; Earth Sciences; Error analysis; Geotechnical Engineering & Applied Earth Sciences; Hydrogeology; Injection; Mathematical Modeling and Industrial Mathematics; Modelling; Multilayers; Multivariate analysis; Neural networks; Original Paper; Permeability; Regression analysis; Salinity; Salinity effects; Sandstone; Sedimentary rocks; Soil Science & Conservation; Training; Water injection
• ISSN: 1420-0597; 1573-1499
• DOI: 10.1007/s10596-024-10312-y

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 . 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 , 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.