Multi-time small-area estimation of oil and gas production capacity by Bayesian multilevel modeling

This paper presents a Bayesian multilevel modeling approach for estimating well-level oil and gas production capacities across multiple time periods within small geographic areas. Focusing on basins, which are economically significant drilling regions, we model the latent production capacity of wells using small-area estimation techniques. Our model accounts for well-level variations within these basins, incorporating factors such as lateral length, water usage, and sand usage. A key aspect of our methodology is the use of the Maidenhead Coordinate System to define small areas, enabling detailed regional analysis. The model was developed and validated using data from the Eagle Ford region, covering the years 2014 to 2019, and demonstrates strong predictive performance, particularly in handling small sample sizes. We expand the model to accommodate temporal dynamics by introducing time-effect parameters, allowing for the analysis of production trends over time. Additionally, we explore the impact of technological advancements by modeling water-sand intensity as a proxy for production efficiency. Our findings suggest that Bayesian multilevel modeling provides robust and flexible tools for understanding and predicting oil and gas production at a granular level, offering valuable insights for energy production forecasting and resource management.