摘要
为了解决页岩气“甜点”分类识别涉及指标多、需要根据个人经验判别、耗时耗力的问题,提出了一种基于随机森林模型的页岩气“甜点”分类方法。首先,选取长宁区的10口井数据,利用肯德尔相关分析筛选出用于识别的11种特征。然后再分别采用单棵决策树和随机森林方法进行预测,得到页岩气“甜点”识别结果。最后,对预测结果分类并进行算法参数优化。实际应用结果表明,单棵决策树预测精度虽可以达到97.7%,但呈现过拟合趋势,且剪枝之后拟合精度大大降低到只有70.7%;采用的随机森林方法避免了单棵决策树的缺陷,并且预测的精度达到98%,而且,计算代价小,能有效降低时间损耗、节省人力成本。证明随机森林机器学习方法结合多源信息是实现页岩气“甜点”识别预测的一种有效手段。
The classification and identification of shale gas“sweet spot”involves a variety of different factors,which requires personal experience,and is usually time and resources consuming.In order to solve this problem,an efficient and effective classification and identification method for shale gas“sweet spot”based on the Random Forest method is proposed.Firstly,data from ten wells in Changning area are selected and eleven features are selected for“sweet spot”classification by the Kendall correlation.Then,the single decision tree and the Random Forest method are used for the“sweet spot”classification and identification.Finally,the results are verified and the Random Forest parameters are optimized.The experimental results show that although the prediction accuracy of a single decision tree can reach 97.7%,it shows a trend of overfitting,and the fitting accuracy is greatly reduced by only 70.7%after pruning.The Random Forest method avoids the disadvantage of the single decision tree method,and the prediction accuracy reaches 98%.Moreover,the computational cost is low,which can effectively reduce the time loss and save the labor cost.As a result,the proposed Random Forest machine learning method with multi-source information is an effective shale gas“sweet spot”classification and identification method.
作者
聂云丽
高国忠
NIE Yunli;GAO Guozhong(Cooperative Innovation Center of Unconventional Oil and Gas,Yangtze University(Ministry of Education&Hubei Province),Wuhan,Hubei 430100,China;College of Geophysics and Petroleum Resources,Yangtze University,Wuhan,Hubei 430100,China)
出处
《油气藏评价与开发》
CSCD
2023年第3期358-367,共10页
Petroleum Reservoir Evaluation and Development
基金
长江大学非常规油气省部共建协同创新中心开放基金“水力压裂裂缝的扩展机理及数值模拟研究”(UOG2022-05)。
关键词
页岩气
“甜点”
机器学习
决策树
随机森林
shale gas
“sweet spot”
machine learning
decision tree
Random Forest
