【摘要】 页岩气藏井间干扰严重制约气井生产，井间干扰程度评价与预测对页岩气高效开发具有重要意义。现有研究主要聚焦页岩气井间干扰现象、生产动态特征以及数值模拟参数优化等方面，但页岩气井间干扰程度定量评价及预测方面的研究较少，且参数体系不全，难以客观评价页岩气井间干扰程度。因此，采用机器学习方法综合考虑地质参数、压裂参数及生产参数，对A页岩气藏井间干扰程度进行评价及预测。先对初始数据进行数据处理，提高数据质量，然后基于处理后的数据，应用聚类分析及随机森林算法评价及预测Y页岩气井间干扰程度。结果表明：A页岩气藏中井间干扰程度低、中、高的井数占比分别为25.93%、37.03%、37.04%，其中压裂因素对A页岩气藏井间干扰程度评价结果影响最大。调参后的页岩气井间干扰程度预测结果达到92.07%，表明所建立的预测模型可应用于实际页岩气井间干扰程度预测，且模型精确度较高，为页岩气井井间干扰量化评价及预测提供了一种有效手段。更多还原

【Abstract】 Inter-well interference seriously affects the production of shale gas wells.The evaluation and prediction of well interference degree is of great significance to the efficient development of shale gas.But the existing research mainly focuses on the interference phenomenon between shale gas wells,production performance,and parameter optimization through numerical simulation.There are few studies on the quantitative evaluation and prediction of the interference degree between shale gas wells,and the selected parameters is incomplete,which makes it difficult to objectively evaluate the well interference between shale gas wells.Therefore,the machine learning method is used to comprehensively consider the geological parameters and fracturing parameters to evaluate and predict the degree of interference between wells in the shale gas reservoir.Firstly,the initial data are processed to improve the data quality.Then,based on the processed data,cluster analysis and random forest algorithm are used to evaluate and predict the interference degree of shale gas wells.The results show that the proportions of the wells with low,medium and high well interference in the shale gas reservoirs are 25.93 %,37.03 % and 37.04 %,respectively.The fracturing factors show significant influence on the well interference degree in the shale gas reservoirs.After parameters optimization,the prediction results of well interference degree reaches 92.07 %,indicating that the developed prediction model can be applied to forecast the well interference degree in shale gas reservoirs.更多还原