The production of gas and oil from the unconventional tight and shale reservoirs is the outcome through a series of cooperative efforts of drilling, completion, and production operations. This study aims to optimize these operations to enhance the well productivity and oil recovery, and ultimately to reduce the development footprint on the level of individual wells. More specifically, a comprehensive data set is collected and analyzed for the Duvernay reservoir, including geology, drilling, completion, production operations, and production data. A customized stacked model is created to train production models with an extreme gradient boosting regressor as the base model and linear regressor as the meta model. The models achieve robust predictive ability with a determination coefficient of up to 0.80. The Shapley values reveal that the producing time, condensate/gas ratio, and the completion section length are the most important features to the early time production. The Bayesian optimization method is adopted to optimize the production using the trained models. This study shows the potential of the machine learning approach to model oil and gas production and provides insights for optimizing production in the tight and shale reservoirs.

非常规致密和页岩储层的天然气和石油生产是一系列钻探，完井和生产作业的合作成果。这项研究旨在优化这些操作，以提高油井生产率和石油采收率，并最终减少单井水平的开发足迹。更具体地说，将收集和分析Duvernay油藏的综合数据集，包括地质，钻探，完井，生产作业和生产数据。创建定制的堆叠模型来训练生产模型，其中极限梯度提升回归模型为基本模型，线性回归模型为元模型。该模型具有高达0.80的确定系数，具有强大的预测能力。Shapley值表明，生产时间，凝析气/天然气比和完井段长度是早期生产的最重要特征。采用贝叶斯优化方法，使用训练后的模型优化生产。这项研究显示了机器学习方法在模拟油气产量方面的潜力，并为优化致密和页岩油藏的生产提供了见识。