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This study evaluates the performance of various machine learning models in predicting the estimated ultimate recovery (EUR) in unconventional shale reservoirs, highlighting the challenges posed by their complex geological features. The analysis shows that Random Forest and XGBoost models significantly outperform others, including Artificial Neural Networks, in terms of accuracy and robustness. The findings suggest that advanced machine learning techniques can enhance production forecasting, aiding engineers

Author

ifeanyichukwu.onyejekwe

Language

EN