The estimate ultimate recovery (EUR) of shale gas in individual well is affected by many factors so that it is difficult to predict accurately. Data-driven methods based on geological and engineering parameters are currently one of the mainstream methods for predicting EUR. However, the importance of early data from gas wells is often overlooked. Therefore, this research set out to use early data, including production and flowback rate data, to develop machine learning models. With the ability to analyze the data by machine learning, the controlling factors on EUR have been analyzed quantitatively. Four schemes have been designed to develop the model, and various machine learning techniques (K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Decision Tree (GBDT)) were applied to process the complex patterns in the data. The results show that, except 30-day flowback rate, the most important factor for EUR is the early production data. The relationship between the early flowback rate and EUR is poor. It is not enough to predict EUR accurately provided that only using the flowback rate data. Good prediction results have been obtained by choosing the most important factors. Among the four algorithms, SVM is considered to be the most reliable model because it is suitable for small data sets and performs well in dealing with nonlinear relationships between variables. The mean absolute percentage error is 13.41% in the test set 49 wells, which can be used as the optimal algorithm for EUR prediction only based on early data.

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使用早期数据开发基于机器学习的页岩气产量预测模型

页岩气单井估算最终采收率（EUR）受多种因素影响，难以准确预测。基于地质和工程参数的数据驱动方法是目前欧元预测的主流方法之一。然而，气井早期数据的重要性常常被忽视。因此，本研究着手利用早期数据，包括生产和回流率数据来开发机器学习模型。借助机器学习分析数据的能力，对欧元的控制因素进行了定量分析。设计了四种方案来开发模型，并应用各种机器学习技术（K 最近邻（KNN）、支持向量机（SVM）、随机森林（RF）、梯度提升决策树（GBDT））来处理数据中的复杂模式。结果显示，除了30天回流率外，对欧元影响最重要的因素是早期的生产数据。早期回流率与欧元关系较差。仅使用回流率数据不足以准确预测欧元。通过选择最重要的因素，获得了良好的预测结果。在四种算法中，SVM 被认为是最可靠的模型，因为它适用于小数据集，并且在处理变量之间的非线性关系方面表现良好。测试集49口井的平均绝对百分比误差为13.41%，可以作为仅基于早期数据的EUR预测的最优算法。