Rock Brittleness Prediction with BDEGTO-Optimized XGBoost

Precise assessment of rock brittleness is a prerequisite for effective wellbore integrity and successful reservoir stimulation in drilling programs. To achieve precise prediction of rock brittleness index (BI), this study proposes an improved optimization algorithm for an artificial gorilla troops optimizer (GTO), called a Bernoulli Differential Evolution Gorilla Troops Optimizer (BDEGTO). In the BDEGTO, Bernoulli mapping is introduced during the population initialization process, and the differential evolution is embedded after the exploration stage of the GTO. These modifications effectively address the early-stage optimization weaknesses and the susceptibility to local optima that are commonly encountered in a traditional GTO. To evaluate the performance of the BDEGTO, comparisons are made with other optimization algorithms based on 91 datasets from 32 rock types. The results demonstrate the significant advantages of the BDEGTO over other algorithms. Furthermore, the BDEGTO is applied to the optimization process of Least Squares Boosting (LSB), Extreme Gradient Boosting (XGB), and Light Gradient Boosting Machine (LGBM). A comparison is made with Support Vector Regression (SVR), Artificial Neural Network (ANN), and Convolutional Neural Network (CNN) algorithms for predicting rock brittleness based on input parameters such as P-wave velocity (Vp), point load index (Is50), and unit weight (UW). The findings indicate that BDEGTO-XGB achieves the best prediction performance for BI. Additionally, through SHapley Additive exPlanations (SHAP) analysis, it is determined that among the three input parameters, Is50 has the most significant influence. These research results provide valuable guidance for the brittleness assessment of similar rocks.