Enhancing wellbore stability through machine learning for sustainable hydrocarbon exploitation

Wellbore instability manifested through formation breakouts and drilling-induced fractures poses serious technical and economic risks in drilling operations. It can lead to non-productive time, stuck pipe incidents, wellbore collapse, and increased mud costs, ultimately compromising operational safety and project profitability. Accurately predicting such instabilities is therefore critical for optimizing drilling strategies and minimizing costly interventions. This study explores the application of machine learning (ML) regression models to predict wellbore instability more accurately, using open-source well data from the Netherlands well Q10-06. The dataset spans a depth range of 2177.80 to 2350.92 m, comprising 1137 data points at 0.1524 m intervals, and integrates composite well logs, real-time drilling parameters, and wellbore trajectory information. Borehole enlargement, defined as the difference between Caliper (CAL) and Bit Size (BS), was used as the target output to represent instability. Twelve regression models were evaluated, including Linear and Polynomial Regression, Decision Tree, Random Forest, Gradient Boosting, Histogram Gradient Boosting, Support Vector Regression, Multi-layer Perceptron, k-Nearest Neighbors, Gaussian and Bernoulli Naive Bayes, and Gaussian Process Regression. Model performance was assessed using the Root Mean Squared Error (RMSE) and Coefficient of Determination (DC). Among them, Histogram Gradient Boosting yielded the highest prediction accuracy (RMSE = 8.5138 ×10^-2 in, DC = 0.99), followed closely by Gradient Boosting, Random Forest, and Decision Tree models. Conversely, Bernoulli Naive Bayes and Support Vector Regression demonstrated poor generalization. To interpret model predictions, SHAP (SHapley Additive exPlanations) analysis was employed, highlighting the most influential features and their directional impacts. The SHAP results aligned closely with heatmap-based feature correlations, confirming that high-performing models considered a diverse set of features, while underperforming models were overly reliant on limited inputs. This study demonstrates that bypassing traditional empirical correlations in data-driven machine learning techniques can enhance prediction accuracy while preserving model interpretability through SHAP analysis.