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.

Surfactant adsorption onto reservoir rocks remains a critical challenge in chemical enhanced oil recovery (cEOR), as it directly impacts flooding efficiency and chemical costs. This study presents a comprehensive scientometric analysis of research on surfactant adsorption for EOR applications over the period 2005–2025. Based on the Scopus database, 877 publications accounting for more than 22,100 citations were retrieved and analyzed to map the intellectual and conceptual structure of this research field. VOSviewer 1.6.20 software was employed to generate keyword co-occurrence networks, author bibliographic coupling, and country-level contributions. The results reveal a strong growth in scientific output after 2016, with annual publications increasing from fewer than 30 papers per year before 2010 to more than 100 papers per year after 2021. “Enhanced Oil Recovery” (165 occurrences), “Adsorption” (101 occurrences), and “Surfactant” (88 occurrences) emerged as the most frequent and highly interconnected keywords. At the geographical level, China (29.4%), the United States (22.3%), and Iran (9.6%) were identified as the leading contributors, together accounting for more than 60% of the global research output. Bibliographic coupling analysis highlighted a core group of highly influential authors shaping the field through strong collaborative networks. Emerging themes such as nanoparticle-assisted EOR, wettability alteration, and low-salinity surfactant systems were identified as rapidly growing research frontiers. This scientometric analysis provides the first quantitative mapping dedicated specifically to adsorption phenomena in cEOR, while highlighting future opportunities for optimizing adsorption control strategies and improving reservoir performance.