Enhancing Well Production Efficiency In The Oil And Gas Industry Through Hybrid Machine Learning Models For Prediction And Optimization

The oil and gas industry faces persistent challenges in accurately predicting and optimizing well production rates amid fluctuating reservoir conditions, data complexity, and operational uncertainties. Traditional physics-based models often struggle with real-time adaptability and computational demands, leading to suboptimal recovery and increased costs. This study addresses the research gap in integrating advanced hybrid machine learning (ML) approaches that combine deep learning with optimization algorithms for multi-well production forecasting and parameter optimization. We propose a hybrid Long Short-Term Memory (LSTM) model optimized with Coyote Optimization Algorithm (COA) and enhanced by feature selection techniques (e.g., SHAP values and mutual information), applied to publicly available Volve field data from Norway. The model incorporates key inputs such as bottomhole pressure, temperature, gas-oil ratio, water cut, and choke settings. Results demonstrate superior performance with an R² of 0.98, RMSE of 2.15 m³/day, and MAE of 1.48 m³/day on blind test wells, outperforming baseline LSTM, XGBoost, and traditional decline curve analysis by 15-25% in accuracy. Optimization scenarios increased predicted production efficiency by up to 18% through dynamic parameter adjustment. This work contributes a scalable, data-driven framework with explainable AI elements, offering both scientific novelty in hybrid methodology and practical value for field operators in mature reservoirs. Future directions include multi-field scaling and integration with digital twins. (Word count: 228)