Prediction of Minimum Horizontal Stress Using Machine Learning for Unconventional Reservoir Applications

This study presents a leakage‐aware machine learning framework for predicting minimum horizontal stress ( σ h min) using structured geomechanical and fracture‐related parameters. A dataset comprising 21,499 records from approximately 200 horizontal wells in the Marcellus Shale was preprocessed using a strictly leakage‐controlled pipeline, including feature refinement, outlier capping, and training‐fold–based transformations. Three ensemble models—random forest (RF), gradient boosting regressor (GBR), and extreme gradient boosting (XGBoost)—along with a stacking regressor and artificial neural network (ANN), were evaluated using root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination ( R 2 ). The RF model achieved the best baseline performance (RMSE = 6.5846, R 2 = 0.9988), while gradient boosting showed improved performance after tuning (RMSE ≈ 6.59). The stacking model delivered competitive results (RMSE = 7.4963), whereas the ANN showed lower performance (RMSE = 55.5008), indicating limited suitability for structured tabular data. A scenario‐based evaluation using Nigerian reservoir data demonstrated reasonable pr3332edictive consistency but was not treated as external validation due to data limitations. The results confirm that leakage‐aware ensemble learning provides a robust and physically consistent approach for predicting minimum horizontal stress, with potential application in reservoir characterization and hydraulic fracturing design.