In recent years, geologists and petroleum engineers have struggled to clearly identify the mechanisms that drive productivity in horizontal, hydraulically-fractured oil wells producing from the middle member of the Bakken Formation. This paper fills a gap in the literature by showing how this play's heterogeneity affects factors that drive well productivity. It is important because understanding the relative strength of productivity drivers and how predictors vary spatially facilitates best-practices for well site selection and well completion design. The paper describes an application of the Random Forest (RF) machine learning technique to identify these mechanisms and to evaluate their importance across nine subareas of the North Dakota portion of the Bakken play. The study examined productivity of 7311 wells initiating production from 2010 through 2017. Well productivity varied considerably across the nine subareas within the play, so it was not surprising that the dominant predictors, the initial 180-day water cut and the 30-day initial gas production, vary spatially to mirror local conditions that strongly affect well productivity. The relative importance of well completion predictor variables, that is, the number of fracture stages per well, volume of injected proppant per stage, volume of injected fluids per stage, and lateral length, varied considerably across the subareas. Statistical permutation tests are presented that generally confirm the importance rankings. Subarea Random Forest models explained from 50 percent to 82 percent of the variation in productivity test samples, while the play-wide model explained 73 percent of the test sample well productivity. Weakness in the predictive ability of the Random Forest models are traced to the limited variability in the training data. Implications of the empirical findings regarding the Bakken play for operators and for research and government institutions are discussed in the concluding section.

近年来，地质学家和石油工程师一直在努力清楚地确定从Bakken地层中段开采的水平，水力压裂油井中提高生产率的机理。本文通过展示该方法的异质性如何影响驱动良好生产率的因素，填补了文献中的空白。这一点很重要，因为了解生产率驱动因素的相对强度以及预测变量在空间上的变化有助于井位选择和完井设计的最佳实践。本文介绍了随机森林（RF）机器学习技术在确定这些机制并评估其在Bakken油田北达科他州的9个子区域中的重要性的应用。该研究调查了从2010年到2017年启动生产的7311口井的生产率。在该生产区内的9个子区域中，井的生产率差异很大，因此，主要的预测指标，最初的180天含水率和30天初始气量并不奇怪。生产，在空间上变化，以反映严重影响油井生产率的当地条件。完井预测变量的相对重要性，即每口井的压裂段数，每段注入的支撑剂量，每段注入的流体量和侧向长度在整个分区中变化很大。提出了统计排列检验，这些检验通常可以确认重要性排名。次区域随机森林模型解释了生产力测试样本变化的50％至82％，而全范围模型解释了73％的测试样品井生产率。随机森林模型的预测能力弱可归因于训练数据的有限变异性。结论部分讨论了有关巴肯油田对运营商，研究机构和政府机构的实证研究结果的含义。