A novel data-driven model for real-time prediction of static Young's modulus applying mud-logging data

Effective drilling planning relies on understanding the rock mechanical properties, typically estimated from petrophysical data. Real-time estimation of these properties, especially static Young's modulus (\({E}_{sta}\)), is crucial for geomechanical modeling, wellbore stability, and cost-effective decision-making. In this study, predictive models of \({E}_{sta}\) were developed using mudlogging data from two vertically drilled wells (A and B) in the same field. \({E}_{sta}\) was estimated from petrophysical data across the studied depth range in both wells using a field-specific equation. Outlier data were identified and removed by evaluating the cross plot of mechanical specific energy and drilling rate for Well A. The data from Well A were then randomly divided into training and testing sets. The algorithms, multi-layer perceptron neural networks, random forests, Gaussian process regression (GPR), and support vector regression, were adjusted and applied to the training data. The resulting models were evaluated on the test data. The GPR model demonstrated the lowest RMSE values in both the training (0.0075 GPa) and testing (0.4577 GPa) phases, indicating superior performance. To further assess the models, the overfitting index and scoring techniques were employed, revealing that the GPR model exhibited the lowest overfitting value and outperformed the other models. Consequently, the GPR model was selected as the best-performing model and was analyzed using Shapley additive explanation to evaluate the influence of each input feature on the output. This analysis indicated that depth had the greatest effect, while rotation speed had the least impact on the model's output. The application of the GPR model to predict \({E}_{sta}\) in Well B demonstrated its high generalization capability. Therefore, it can be confidently stated that with additional data, this model could be effectively applied to similar depth ranges in other wells within the field. The study introduces innovations by applying GPR to predict \({E}_{sta}\) from mudlogging data, addressing outlier impact on predictions, and developing a real-time \({E}_{sta}\) prediction model for drilling.