This paper describes a practical approach for implementing stochastic determination of GGV production performances and for generalizing the prediction capability of deterministic models. Deterministic site-specific models were derived by using the GGV module in the recently developed MCP (Methane Control and Prediction) software suite. These models were generated using multi-parameter regression techniques and were then improved by inclusion of extra input parameters that eliminated the site dependency and improved the predictions. Statistical distributions of input parameters in these models were quantified and tested with the Kolmogorov–Smirnov goodness-of-fit technique. Next, Monte Carlo simulations were performed using these distributions and generalized results for GGV performances were generated. The results of this work indicate that this approach is a promising method of representing the variability in GGV performances and to improve the limited and site-specific character of the deterministic models.