The ultra-low permeability nature of shale reservoirs leads to an extended linear flow and necessitates horizontal wells with multi-stage engineered fractures to efficiently extract hydrocarbons resources. These artificially-generated and naturally-occurring fractures form complex networks that create complex flow regimes which control oil production. These fractures are neither identical nor equally-spaced, which leads to a production profile with a masked onset of the boundary-dominated flow. The combination of the extended linear flow with the indeterminate onset of the boundary-dominated flow challenges the current deterministic analytic approaches to forecast the estimated ultimate recovery (EUR). Herein, we propose a novel machine-learning approach which overcomes these challenges and provides reliable EUR estimates based on field-wide analyses. We implement a novel unsupervised machine learning (ML) methodology, which allows for automatic identification of the optimal number of features (signals) present in the data based on non-negative matrix/tensor factorization coupled with k-means clustering incorporating regularization and physics constraints. In the presented analyses, the input data to the ML algorithm is the available (public) production history from the field collected at existing unconventional reservoirs. We validate our approach through hindcasting of the production data, where we achieved an excellent agreement. In addition, our approach is able to identify the poorly-performing wells, which could benefit from early refracing. Our approach provides fast and accurate estimations of the well performance without presumptions about the state of the well or the flow regime.

页岩储层的超低渗透性导致线性流动的扩展，并需要水平井进行多级工程压裂，以有效地开采油气资源。这些人工产生和自然产生的裂缝形成了复杂的网络，形成了控制石油生产的复杂流态。这些裂缝既不相同也不等距，从而导致生产剖面的边界支配流开始被掩盖。扩展的线性流动与边界支配流动的不确定开始相结合，挑战了目前用于预测估计最终采收率（EUR）的确定性分析方法。在此处，我们提出了一种新颖的机器学习方法，该方法可以克服这些挑战，并基于现场分析提供可靠的欧元估算值。我们实施了一种新颖的无监督机器学习（ML）方法，该方法可基于非负矩阵/张量分解以及与之相关的数据，自动识别数据中存在的最佳特征（信号）数量。结合正则化和物理约束的k均值聚类。在提出的分析中，ML算法的输入数据是在现有非常规油藏中采集的油田的可用（公共）生产历史。我们通过对生产数据进行后验来验证我们的方法，并在此达成了良好的协议。此外，我们的方法能够识别性能较差的井，这些井可能会因早期重建而受益。我们的方法无需对井的状态或流态进行任何假设，即可快速，准确地估算井的性能。