Comparative Analysis of Crude Oil and Gas Production Prediction Using Various Machine Learning Models

Accurate forecasting of crude oil and gas production is critical to the strategic planning and operational efficiency of the energy industry. Traditional statistical approaches often fall short in capturing the non-linear and dynamic patterns inherent in petroleum production data. This research paper explores the application of machine learning (ML) and deep learning techniques to predict petroleum and gas production more accurately using historical and geological datasets. The study conducts a comparative analysis of four predictive models—Linear Regression, Random Forest, XGBoost, and LSTM—based on their performance metrics, including R-squared (R²), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). The research methodology includes data preprocessing, normalization, model training, and validation using an 80-20 train-test split. The models are evaluated not only in terms of predictive accuracy but also in their ability to handle complex data structures. To enhance practical usability, the models are integrated into an interactive Streamlit dashboard that enables real-time prediction and visualization. Among the evaluated models, LSTM demonstrated superior performance due to its ability to capture time-series dependencies effectively. This paper concludes that deep learning approaches, when combined with interactive analytics tools, offer a robust framework for production forecasting in the energy sector.