Traditional decline curve analyses (DCAs), both deterministic and probabilistic, use specific models to fit production data for production forecasting. Various decline curve models have been applied for unconventional wells, including the Arps model, stretched exponential model, Duong model, and combined capacitance-resistance model. However, it is not straightforward to determine which model should be used, as multiple models may fit a dataset equally well but provide different forecasts, and hastily selecting a model for probabilistic DCA can underestimate the uncertainty in a production forecast. Data science, machine learning, and artificial intelligence are revolutionizing the oil and gas industry by utilizing computing power more effectively and efficiently. We propose a data-driven approach in this paper to performing short term predictions for unconventional oil production. Two states of the art level models have tested: DeepAR and used Prophet time series analysis on petroleum production data. Compared with the traditional approach using decline curve models, the machine learning approach can be regarded as” model-free” (non-parametric) because the pre-determination of decline curve models is not required. The main goal of this work is to develop and apply neural networks and time series techniques to oil well data without having substantial knowledge regarding the extraction process or physical relationship between the geological and dynamic parameters. For evaluation and verification purpose, The proposed method is applied to a selected well of Midland fields from the USA. By comparing our results, we can infer that both DeepAR and Prophet analysis are useful for gaining a better understanding of the behavior of oil wells, and can mitigate over/underestimates resulting from using a single decline curve model for forecasting. In addition, the proposed approach performs well in spreading model uncertainty to uncertainty in production forecasting; that is, we end up with a forecast which outperforms the standard DCA methods.

确定性和概率性的传统下降曲线分析（DCA）使用特定的模型来拟合生产数据以进行生产预测。各种下降曲线模型已经应用于非常规井，包括Arps模型，拉伸指数模型，Duong模型和组合电容-电阻模型。但是，确定使用哪种模型并不是一件容易的事，因为多个模型可以很好地拟合数据集，但提供不同的预测，并且匆忙为概率DCA选择模型可能会低估生产预测中的不确定性。数据科学，机器学习和人工智能通过更有效地利用计算能力，正在彻底改变石油和天然气行业。在本文中，我们提出了一种数据驱动的方法来对非常规石油生产进行短期预测。已测试了两个最新水平的模型：DeepAR和对石油生产数据进行了Prophet时间序列分析。与使用下降曲线模型的传统方法相比，机器学习方法可以视为“无模型”（非参数），因为不需要预先确定下降曲线模型。这项工作的主要目标是开发神经网络和时间序列技术并将其应用于油井数据，而又不具备有关提取过程或地质与动态参数之间的物理关系的大量知识。出于评估和验证的目的，将所提出的方法应用于美国Midland油田的选定井中。通过比较我们的结果，我们可以推断DeepAR和Prophet分析都有助于更好地了解油井的行为，并且可以减轻由于使用单个下降曲线模型进行预测而导致的过高/过低的估计。此外，所提出的方法在将模型不确定性扩展到生产预测的不确定性方面表现良好。也就是说，我们得出的预测要优于标准DCA方法。