Horizontal well fracturing technology is widely used in unconventional reservoirs such as tight or shale oil and gas reservoirs. Meanwhile, the potential of enhanced oil recovery (EOR) methods including huff-n-puff miscible gas injection are used to further increase oil recovery in unconventional reservoirs. The complexities of hydraulic fracture properties and multiphase flow make it difficult and time-consuming to understand the well performance (i.e., well production) in fractured shale reservoirs, especially when using conventional numerical methods. Therefore, in this paper, two methods are developed to bridge this gap by using the machine learning technique to forecast well production performance in unconventional reservoirs, especially on the EOR pilot projects. The first method is the artificial neural network, through which we can analyze the big data from unconventional reservoirs to understand the underlying patterns and relationships. A bunch of factors is contained such as hydraulic fracture parameters, well completion, and production data. Then, feature selection is performed to determine the key factors. Finally, the artificial neural network is used to determine the relationship between key factors and well production performance. The second is time series analysis. Since the properties of the unconventional reservoir are the function of time such as fluid properties and reservoir pressure, it is quite suitable to apply the time series analysis to understand the well production performance. Training and test data are from over 10000 wells in different fractured shale reservoirs, including Bakken, Eagle Ford, and Barnett. The results demonstrate that there is a good match between the available and predicated well performance data. The overall values of the artificial neural network and time series analysis are both above 0.8, indicating that both methods can provide reliable results for the prediction of well performance in fractured shale reservoirs. Especially, when dealing with the EOR field cases, such as huff-n-puff miscible gas injection, Time series analysis can provide more accurate results than the artificial neural network. This paper presents a thorough analysis of the feasibility of machine learning in multiple fractured shale reservoirs. Instead of using the time-consuming numerical methods, it also provides a more robust way and meaningful reference for the evaluation of the well performance.

中文翻译：

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从数值方法到机器学习方法的油井性能：在多个压裂页岩储层中的应用

水平井压裂技术广泛应用于致密或页岩油气藏等非常规油气藏。同时，包括huff-n-puff混相气体注入在内的提高石油采收率（EOR）方法的潜力可用于进一步提高非常规油藏的石油采收率。水力压裂特性和多相流的复杂性使得了解压裂页岩储层的井性能（即井产量）变得困难且耗时，尤其是在使用传统数值方法时。因此，本文开发了两种方法，通过使用机器学习技术来预测非常规油​​藏特别是 EOR 试点项目的油井生产动态，以弥补这一差距。第一种方法是人工神经网络，通过它我们可以分析来自非常规油藏的大数据，以了解潜在的模式和关系。包含一系列因素，例如水力压裂参数、完井和生产数据。然后，进行特征选择以确定关键因素。最后，利用人工神经网络确定关键因素与油井生产性能之间的关系。二是时间序列分析。由于非常规储层的性质是流体性质、储层压力等时间的函数，因此非常适合应用时间序列分析来了解井生产动态。训练和测试数据来自不同裂缝性页岩油藏的 10000 多口井，包括 Bakken、Eagle Ford 和 Barnett。结果表明，可用的和预测的井动态数据之间存在良好的匹配。整体人工神经网络和时间序列分析的值均在0.8以上，表明两种方法都能为裂缝性页岩储层的井况预测提供可靠的结果。特别是在处理 EOR 现场情况时，例如 huff-n-puff 混相气体注入，时间序列分析可以提供比人工神经网络更准确的结果。本文全面分析了机器学习在多个裂缝性页岩储层中的可行性。替代了耗时的数值方法，它也为井况评价提供了更稳健的方式和更有意义的参考。