Real-Time Prediction of Near-Bit Rock Geo-Mechanical Properties from Drilling and Gamma Ray Data: A Comparative Analysis Using Machine Learning and Deep Learning Techniques
Real-time estimation of rock geomechanical properties plays a vital role in evaluating wellbore stability, optimizing drilling operations, completion design and maintaining formation integrity, thereby enhancing overall drilling efficiency. Conventional empirical correlations often lack the accuracy and real-time predictive capability required for key properties such as young's modulus. Furthermore, these correlations rely on input parameters such as compressional and shear wave travel times and bulk density that are typically measured approximately 90 ft behind the drill bit during drilling operations. This study presents a robust model for real-time prediction of a computed static Young's modulus while drilling, using near-bit drilling parameters and gamma ray measurements. A total of 6,279 datasets from five wells in the Volve Field were collected for this study. The database consists of logging-while-drilling measurements and drilling parameters. The model predictors include standpipe pressure, rate of penetration, weight on bit, surface torque, gamma ray, true formation resistivity and equivalent circulating density. The input features were selected based on their proximity to the drill bit and their strong influence on the target variables. As an initial step, exploratory and preprocessing data analysis was performed to investigate the relationships between the input features and the target variables, and to remove outliers and abnormal data. Data from five wells were used for model development (randomly split into 80% for training and 20% for testing), while one additional well was reserved as blind validation sets. For model development, both supervised regression machine learning algorithms (Extreme Gradient Boosting and Random Forest) and deep learning algorithms (multilayer perceptron neural networks) were trained and optimized for real-time prediction of young's modulus. The results show that the XGB, ET and RF models achieved high accuracy (R2 = 0.99 – 1.00) on the training dataset but experienced a notable decrease to approximately 0.91 on the validation dataset. The multilayer perceptron (MLP) achieved a consistent accuracy of 0.87 on both the training and validation datasets. Comparative analysis revealed that XGB, ET and RF exhibited mild overfitting, indicated by approximately 10% accuracy drop between training and validation. On the blind test, the optimized MLP model demonstrated superior generalization performance compared to RF and XGB. Model evaluation confirmed that the MLP accurately captured depth-dependent variations of young's modulus in offset wells, with maximum mean absolute percentage errors (MAPE) of 4.32%. This study presents a robust and reliable machine learning framework for real-time prediction of young's modulus, reducing dependence on core testing and enabling dynamic decision-making during drilling operations. The proposed method offers a scalable approach for intelligent formation characterization and can be seamlessly integrated into real-time drilling advisory systems. Its implementation supports confident drilling and mitigates uncertainties related to wellbore instability, ultimately reducing non-productive time and overall operational costs.