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Productivity forecast for multi-stage fracturing in shale gas wells based on a random forest algorithm
Energy Sources, Part A: Recovery, Utilization, and Environmental Effects ( IF 2.9 ) Pub Date : 2020-11-24
Ju-Hua Li, Lei Ji

ABSTRACT

This study addresses the problem of low-accuracy production forecasting for horizontal shale gas wells by applying a random forest algorithm and considering both the physical reservoir parameters and the engineering fracturing parameters. Based on the interpretation of logging data, drilling data, fracturing data, and sectional production data from the Fuling shale gas field, the random forest algorithm was used to build a regression model, through partial correlation analysis and recursive feature elimination to select the optimal key influencing factors. The study revealed that the layer, cluster spacing, number of clusters, 40/70 mesh low-density ceramsite (medium sand), and total proppant are the key factors influencing sectional production, and they all have different effects on it. The random forest algorithm showed better prediction capacity than the other algorithms. Its coefficient of determination and root-mean-square error on the test set were 0.723 and 0.319, respectively, which indicates that it is an effective approach and has good generalization ability. The prediction results of the productivity regression model based on the random forest algorithm, combined with the trend surface response analysis, were used to obtain the optimal values of the six key fracturing parameters of the unfractured well.



中文翻译:

基于随机森林算法的页岩气井多段压裂产能预测

摘要

本研究通过应用随机森林算法并同时考虑了物理储层参数和工程压裂参数,解决了水平页岩气井产量预测不准确的问题。在对ling陵页岩气田测井数据,钻井数据,压裂数据和分段生产数据进行解释的基础上,采用随机森林算法建立回归模型,通过偏相关分析和递归特征消除选择最优关键。影响因素。研究表明,层,团簇间距,团簇数量,40/70目低密度陶粒(中砂)和总支撑剂是影响断面生产的关键因素,它们对断​​面的影响各不相同。随机森林算法显示出比其他算法更好的预测能力。在测试集上的确定系数和均方根误差分别为0.723和0.319,这表明这是一种有效的方法,并且具有良好的泛化能力。利用基于随机森林算法的产能回归模型的预测结果,结合趋势面响应分析,获得了未压裂井六个关键压裂参数的最优值。

更新日期:2020-11-25
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