This paper presents a Bayesian multilevel modeling approach for estimating well-level oil and gas production capacities across small geographic areas over multiple time periods. Focusing on a basin, which is a geologically and economically distinct drilling region, we model the production level of wells grouped by area and time, using priors as regulators of inferences. Our model accounts for area-level and time-level variations as well as well-level variations, incorporating lateral length, water usage, and sand usage. The Maidenhead Coordinate System is used to define uniform (small) geographic areas, many of which contain only a small number of wells in a given time period. The Bayesian small-area model is first built and checked, using data from the Bakken region, covering from 21 February 2012 to 12 June 2024. The model is expanded to accommodate temporal dynamics by introducing time-effect components, allowing for the analysis of production trends over times. We explore the impact of technological advancements by modeling water-sand intensity as a proxy for production efficiency. The Bayesian multilevel modeling approach provides a robust and flexible tool for modeling oil or/and gas production at area and time levels, informing the energy production prediction with uncertainties.

翻译：本文提出了一种贝叶斯多层建模方法，用于估算多个时间段内小地理区域井级的油气产能。我们以地质和经济上独特的钻井区域——盆地为研究对象，通过将先验分布作为推断调节器，对按区域和时间分组的油井生产水平进行建模。该模型同时考虑了区域层面、时间层面和井层面的变异，并纳入了水平段长度、用水量和用砂量等参数。研究采用梅登黑德坐标系定义均匀的小型地理区域，其中许多区域在特定时间段内仅包含少量油井。首先利用巴肯地区2012年2月21日至2024年6月12日的数据构建并验证了贝叶斯小区域模型。通过引入时间效应分量扩展模型以处理时间动态特性，从而实现对生产趋势的跨时分析。我们通过将水砂强度作为生产效率的代理变量进行建模，探讨了技术进步的影响。贝叶斯多层建模方法为区域和时间层面的油气生产建模提供了稳健灵活的工具，可为包含不确定性的能源生产预测提供依据。