Abstract
This research paper investigates the effectiveness of Bayesian modeling techniques for estimating gas production variables in challenging low-data environments and complex systems, with a specific focus on the Haynesville Basin. The study aims to evaluate the superiority of Bayesian models in handling uncertainty and quantifying risks associated with gas production estimation by utilizing early-stage data and petrophysical information to determine initial production performance, b-factor, decline parameters, and EUR. The findings of this study provide valuable insights for industry decision-makers, facilitating informed investment decisions.
The research employs a Bayesian model to predict gas production variables under different development scenarios and with both informative and non-informative priors. Various time data slices are selected from different points in the Basins' development history, and the results are compared with actual data and frequentist models generated at these different time slices. The study's conclusions highlight the superiority of Bayesian techniques in modeling and estimating gas production, as they effectively handle uncertainty and offer a clearer understanding of risks in low-data environments and complex systems. In contrast, frequentist models are prone to overtraining, particularly in the early stages, making them less suitable for such scenarios. Furthermore, the study shows that non-informative priors lead to more conservative results with wider High-Density Intervals (HDIs) on most parameters, while informative priors may introduce biases. These findings emphasize the importance of leveraging distributions instead of individual observations, especially in limited data applications.
This research contributes to the existing literature by demonstrating the practical application of Bayesian modeling in gas production estimation and underscoring its significance in basins with limited or highly heterogeneous data. The paper's innovative approach to gas production modeling and estimation offers valuable insights, making it a valuable addition to the industry and providing guidance for future investment decisions.
