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This project report presents a hybrid approach combining numerical simulation and machine learning to enhance shale gas production prediction and optimization. The study utilizes Gaussian Process Regression (GPR) and Support Vector Machine (SVM) models to analyze production data and optimize parameters, demonstrating significant computational efficiency and accuracy in predictions. The results validate the effectiveness of machine learning in optimizing shale gas development strategies while reducing relian

Author

24mt0378

Language

EN