基于机器学习的煤系致密砂岩气储层分类研究——以鄂尔多斯盆地DJ区块为例

Research on classification of tight sandstone gas reservoir in coal measures based on machine learning:a case from DJ Block of Ordos Basin

鄂尔多斯盆地DJ区块煤系致密砂岩气储层为特低孔隙度和渗透率储层,强非均质性是储层预测和评价的关键难题。利用1880块岩芯柱塞孔渗及33组压汞分析资料,计算了流动层段指数和孔喉半径概率累积分布函数,优选了基于流动层段指数(FZI)的流动单元3分类方案;为解决不同类型砂岩样本数量失衡的问题,对734组宏观岩石相等类别型数据及数值型测井数据进行了二次分层抽样均化采样处理,重构了数据子集;通过参数设定—训练—建模—测试—性能评估的交互验证法优选了建模参数,构建了无监督和有监督2类7种算法的机器学习模型,分别为K均值聚类、朴素贝叶斯、决策树、支持向量机、深度学习、随机森林和梯度提升树;并依据平衡分数f_(1)得分大小确定了梯度提升树学习和深度学习2个最优分类模型。研究结果表明:基于FZI流动单元的砂岩分类,能显著降低非均质性对渗透率预测精度的影响;与无监督方法相比,有监督的、积极的机器学习类方法更契合砂岩流动单元分类原则;梯度提升树学习算法能利用岩石相、成岩相、沉积微相等类别型数据建模,可用于流动单元成因特征等基础地质方面的研究,而深度学习对测井等数值型数据处理能力更强,适合于渗透率预测等工程应用;实现致密砂岩流动单元合理分类的机器学习模型并不惟一,可结合数据类型和研究目的优选算法独立构建。

The coal-measure tight sandstone in the DJ block of the Ordos Basin is an ultra-low porosity and permeability reservoir,and strong heterogeneity is a key problem in reservoir prediction and evaluation.Based on 1880 rock cores plug porosity and permeability anal⁃ysis data and 33 sets of mercury intrusion analysis data,the probability cumulative distribution function of the flow interval index and pore throat radius was calculated,and the three flow units classification scheme with FZI was optimized.Tosolve the problem of the imbalance of the number of different types of sandstone samples,734 sets of macro-rock equivalent type data and numerical logging data were sub⁃jected to sub-stratified sampling and homogenization sampling processing,and the data subset was reconstructed;Through the interactive verification method of parameter setting-training-modeling-testing-performance evaluation,the modeling parameters were optimized,and machine learning models of 7 algorithms in two categories,unsupervised and supervised,were constructed.They are K-means clustering,naive Bayes,decision tree,support vector machine,deep learning,random forest and gradient tree boosting respectively,and the two op⁃timal classifications of gradient boosting tree learning and deep learning are determined according to the f_(1) score value.The research results show thatthe sandstone classification based on FZI flow unit can significantly reduce the influence of heterogeneity on the accuracy of per⁃meability prediction;compared with unsupervised method,the supervised and active machine learning methods are more suitable for the classification principle of flow unit.Gradient boosting tree learning algorithm can use lithofacies,diagenetic facies,sedimentary micro-e⁃qual type data modeling,and can be used for basic geological research such as the genetic characteristics of flow units,while deep learn⁃ing has stronger processing capabilities for numerical data such as well loggingand is suitable for engineering applications such as permea⁃bility prediction.The machine learning model with efficient and reasonable classification for flow units is not unique and can be constructed independently according to research purpose and data typesto optimize the algorithm.