Synergizing Machine Learning and Physical Models for Enhanced Gas Production Forecasting: A Comparative Study of Short- and Long-Term Feasibility

Advanced strategies for production forecasting, operational optimization, and decision-making enhancement have been employed through reservoir management and machine learning (ML) techniques. A hybrid model is established to predict future gas output in a gas reservoir through historical production data, including reservoir pressure, cumulative gas production, and cumulative water production for 67 months. The procedure starts with data preprocessing and applies seasonal exponential smoothing (SES) to capture seasonality and trends in production data, while an Artificial Neural Network (ANN) captures complicated spatiotemporal connections. The history replication in the models is quantified for accuracy through metric keys such as mean absolute error (MAE), root mean square error (RMSE), and R-squared. The future forecast is compared with an outcome of a previous physical model that integrates wells and reservoir properties to simulate gas production using regressions and forecasts based on empirical and theoretical relationships. Regression analysis ensures alignment between historical data and model predictions, forming a baseline for hybrid model performance evaluation. The results reveal the complementary attributes of these methodologies, providing insights into integrating data-driven and physics-based approaches for optimal reservoir management. The hybrid model captured the production rate conservatively with an extra margin of three years in favor of the physical model.