Surfactants Adsorption onto Algerian Rock Reservoir for Enhanced Oil Recovery Applications: Prediction and Optimization Using Design of Experiments, Artificial Neural Networks, and Genetic Algorithm (GA)

This study investigates the adsorption of surfactants on Algerian reservoir rock from Hassi Messaoud. A new data generation method based on a design of experiments (DOE) approach has been developed to improve the accuracy of adsorption modeling using artificial neural networks (ANNs). Unlike traditional data acquisition methods, this approach enables a methodical and structured exploration of adsorption behavior while reducing the number of required experiments, leading to improved prediction accuracy, optimization, and cost-effectiveness. The modeling is based on three key parameters: surfactant type (SDS and EOR ASP 5100), concentration, and temperature. The dataset required for ANN training was generated from a polynomial model derived from a full factorial design (DOE) established in a previous study. Before training, 32 different ANN configurations were evaluated by varying learning algorithms, adaptation functions, and transfer functions. The best-performing model was a cascade-type network employing the Levenberg–Marquardt learning function, learngdm adaptation, tansig activation function for the hidden layer, and purelin for the output layer, achieving an R2 of 0.99 and an MSE of 6.84028 × 10−9. Compared to DOE-based models, ANN exhibited superior predictive accuracy, with a performance factor (PF/3) of 0.00157 and the same MSE. While DOE showed a slight advantage in relative error (9.10 × 10−5% vs. 1.88 × 10−4% for ANN), ANN proved more effective overall. Three optimization approaches—ANN-GA, DOE-GA, and DOE-DF (desirability function)—were compared, all converging to the same optimal conditions (SDS at 200 ppm and 25 °C). This similarity between the various optimization techniques confirms the strength of genetic algorithms for optimization in the field of EOR and that they can be reliably applied in practical field operations. However, ANN-GA exhibited slightly better convergence, achieving a fitness value of 2.3247.