Soft Computing Approach for the Prediction of Surfactant Adsorption

Surfactants are commonly used in chemical enhanced oil recovery (cEOR). The quantity of surfactant loss due to adsorption on a rock directly influences a cEOR project economics. Therefore, surfactant adsorption quantification is an important area of interest. Surfactant adsorption is greatly influenced by the mineral composition present in the rock. This paper presents a novel machine learning (ML) intelligent model to predict surfactant adsorption as a function of mineral composition, maximum adsorption capacity, and surfactant concentration. Several pure minerals were used to determine the static adsorption of a novel cationic Gemini surfactant. The novel surfactant is compatible with high salinity and high-temperature environment. XRD was utilized to show the percentage of the rock-forming minerals. The solid-liquid ratio used in this study was 1 gm in 15 ml, and the time given for rock fluid interaction was 24 hours. The supernatants obtained after 24 hours of shaking and 20 minutes of centrifuging were analyzed using high-performance liquid chromatography to determine the remaining surfactant concentration. ML algorithms were applied to the dataset to predict surfactant adsorption. Hyperparameters tuning was performed using K-fold cross-validation integrated with an exhaustive grid search technique. Surfactant adsorption isotherms were constructed from the real experimental data for each pure mineral. The dataset was divided into an 80:20 ratio for training and testing, respectively. Extreme Gradient Boosting (XGBoost) and random forest (RF) techniques were applied to the training dataset to predict the surfactant adsorption as a function of mineral composition, maximum adsorption capacity, and surfactant concentration. The remaining 20% of the dataset was used to test the models. The evaluation error metrics comprising R² and RMSE showed good agreement of predictions with the unseen data. Also, it was found that XGBoost outperformed RF in surfactant adsorption predictions with R² of 0.9914 and 0.8990, respectively. The developed model can be used to predict surfactant adsorption by using mineral composition and surfactant concentration. The developed model saves a significant amount of time in running the tedious and time-consuming experiments and helps to provide a good quick estimate of surfactant adsorption. This model will add a great value in the practical application of a chemical EOR project.