【摘要】 页岩油的储层改造是后续开发的关键，然而影响储层产能的因素众多，不同水平井产能主控因素尚不明确，从而使得产能预测精度不高。为了明确页岩油水平井初期产能控制因素，以鄂尔多斯盆地长7段为研究对象，采用Pearson相关分析、主成分分析、灰色关联法对产能影响因素进行筛选降维，并基于筛选结果，建立机器学习预测模型，对初期产能进行合理预测。研究表明：通过权衡不同方法获得的各因素间的关联度，最终确定段数、每米加砂量、含油饱和度、焖井时间、抽深、孔隙度为该区域的产能主控因素；以筛选的主控因素作为输入参数，基于5种单一机器学习算法建立的融合模型有着较高的预测精度，其R~2、RMSE(root mean squared error)和MRE(mean relative error)分别达到了0.958 1、0.173 8、1.119 1%;相较于单一的机器学习算法模型，融合模型能够更好地对水平井初期产能进行预测，提升预测精度，后续可结合多元数据优化模型算法提升模型预测效果，对实际页岩油水平井开发优化具有重要指导意义。更多还原

【Abstract】 Reservoir stimulation of shale oil is the key to subsequent development. However, there are numerous factors affecting reservoir productivity, and the main controlling factors of productivity for different horizontal wells remain unclear, leading to low accuracy in productivity prediction. To identify the main controlling factors of initial productivity of shale oil horizontal wells, taking the Chang 7 Member of the Ordos Basin as the research object, Pearson correlation analysis, principal component analysis, and grey relational analysis were used to screen and reduce the dimensionality of productivity-influencing factors. Based on the screening results, a machine learning prediction model was established to reasonably predict the initial productivity. The results show these as follows. By weighing the correlation degree between various factors obtained by different methods, the number of fracturing stages, proppant dosage per meter, oil saturation, soaking time, pumping depth, and porosity are finally determined as the main controlling factors of productivity in this area; taking the screened main controlling factors as input parameters, the integrated model established based on 5 single machine learning algorithms has high prediction accuracy, with R~2, RMSE(root mean squared error), and MRE(mean relative error) reaching 0.958 1, 0.173 8, and 1.119 1% respectively; compared with single machine learning algorithm models, the integrated model can better predict the initial productivity of horizontal wells and improve prediction accuracy. In the future, the model algorithm can be optimized by combining multi-source data to enhance the prediction effect, which has important guiding significance for the optimization of actual shale oil horizontal well development.更多还原