Real-Time Prediction of Rock Young Modulus and Uniaxial Compressive Strength from Artificial Intelligence-Based Correlations

Abstract

Rock geomechanical properties are vital parameters used for field development studies. Young's modulus (E) and uniaxial compressive strength (UCS) are two of the most fundamental variables used to characterize rock strength and formation deformation behavior. The conventional laboratory measurements of these parameters are costly, time-consuming and often not available during drilling operations. Consequently, real-time estimation of Static young modulus and UCS are crucial for drilling optimization, wellbore stability analysis and fracture containment assessment. This study investigates an artificial intelligence (AI) based approach for real-time prediction of Young's modulus and UCS using drilling and logging data. Ensemble supervised machine learning models were assessed to find the most accurate predictive framework. The Machine Learning models were developed using offset field dataset from five wells, comprising approximately 5,600 depth-indexed samples. The input variables includes rate of penetration (ROP), weight on bit (WOB), torque (T), Equivalent circulation density (ECD) and Gamma Ray (GR) and acoustic log responses of density and neutron. Four machine learning algorithms were investigated and compared, including Extreme gradient boosting (XGBoost), Random forest (RF), Extreme trees (ET) and Categorical Boosting (CB). The datasets were preprocessed, normalized and divided into training and testing subsets using 80:20 split respectively. During the model development, hyperparameter tuning was performed to optimize model performance and two independent wells not used during model development were used for blind testing to evaluate the generalization capability of the models. The results showed that all algorithms achieved a coefficient of determination (R2 >0.90) on the test datasets with ET and CB provided the best overall predictive performance. The optimized models achieved coefficients of determination (R2) of 0.88 for UCS and 0.875 for Young's modulus, with corresponding root mean square errors of 1484 psi and 3.59 GPa, respectively. Features importance analysis revealed that neutron porosity (NPHI), formation bulk density (RHOB) and Gamma Ray are the three most influential predictors. The proposed framework enables continuous, depth-based estimation of rock mechanical properties in real time, supporting improved geomechanical modeling and drilling optimization. The approach shows strong potential for integration into real-time drilling advisory systems for autonomous drilling workflows