Stacking modeling with genetic algorithm-based hyperparameter tuning for uniaxial compressive strength prediction

Measuring rock strength using an uniaxial testing machine is destructive and costly, requiring high-quality rock samples. This work suggests an alternate approach that makes use of machine learning techniques to predict uniaxial compressive strength (UCS). The input parameters for this investigation were derived from 180 datasets containing well log variables such as resistivity (RT), sonic travel time (DT), and gamma-ray (GR), as well as rock properties like density. All these datasets came from a shaly sand reservoir in the Bengal Basin. To forecast UCS, a number of methods were used, such as multilayer perceptron (MLP), random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost), and multiple variable regression (MVR). Additionally, a hybrid stacking model that combines these algorithms was developed. Hyperparameter optimization was conducted using grid search and genetic algorithm. A notable contribution of this study lies in the application of both grid search and genetic algorithm (GA) for hyperparameter optimization, implemented across both individual base learners and the stacking ensemble model. Regression metrics including coefficient of determination (R²), mean absolute error (MAE), root mean square error (RMSE), maximum error (MaxE), and minimum error (MinE) were used to assess the effectiveness of the models. The proposed stacking model achieved a high testing R² of 0.9762, outperforming individual models. The methodology provided in this paper can assist engineers and researchers in quickly and precisely determining the strength of reservoir rock by using a few log features, hence decreasing the reliance on labor-intensive and time-consuming laboratory work.