Intelligent identification methods for shale lithology based on the coupling deeply of logging curves
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摘要:
四川盆地渝西区块五峰组-龙马溪组是国内典型的页岩气储层，其层间强非均质性，导致采集的测井曲线信息存在大量冗余且曲线间耦合关系复杂，岩相测井识别难度高、精度低，亟需技术方法创新。本文在岩相划分与分析的基础上，联合主成分分析法与随机森林算法构建了一种岩相智能识别方法。研究结果表明：①利用主成分分析法对测井曲线进行优化，可以使测井曲线深度耦合，削减测井信息冗余及曲线间复杂耦合关系等因素对岩相识别的影响，可得到更加科学有效的数据信息；②向原始数据添加不改变其岩相的微量变化，可以达到数据增强的效果，在一定程度上解决随机森林算法由于数据集比较小或者不平衡时，模型的泛化能力和稳定性差的问题；③联合主成分分析法与随机森林算法构建的岩相智能识别方法运用识别准确率达83%以上，适用性强，准确率高。该方法不仅在一定程度上克服了研究区岩相识别困难的问题，也极大地提高了岩相识别效率，对促进研究区页岩气经济高效开发具有重要意义。
Abstract:Objective The Wufeng-Longmaxi formations in the Yuxi Block of the Sichuan Basin, China are typical shale gas reservoirs. The strong heterogeneities of these formations leads to both information redundancy and complex coupling relationships of logging curves, which is challenging and inaccurate for traditional lithofacies identification.
Methods This study developed an intelligent lithofacies identification method that integrated with both principal component analysis (PCA) and the random forest algorithm based on lithofacies classification and analysis.
Results Research findings were given as follows: First, PCA optimization can strengthen the coupling of logging curves, reducing the impact of lithofacies identification such as logging curve information redundancy and complex relationships . Second, data augmentation was achieved by including minor changes to the original data without impacting lithofacies, improving model generalization and stability during handling small or imbalanced datasets. Finally, lithofacies identification accuracy based on PCA with the random forest algorithm achievedabove 83%, with a high precision and a strong applicability.
Conclusion This method not only overcomes the difficulty of lithofacies identification in the study area, but also greatly improves the efficiency of lithofacies identification, which is of great significance for promoting the economic and efficient development of shale gas in the study area.
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图 2 主成分分析与随机森林算法流程
Figure 2. Workflow for both principal component analysis and random forest algorithm
图 3 X1、X2、X3、X5井页岩岩相划分
Figure 3. Shale lithofacies classification of Wells X1, X2, X3, and X5
图 4 X1、X2、X3、X4、X5井连井对比图
Figure 4. Comparison of well connections for Wells X1, X2, X3, X4, and X5
图 5 不同页岩岩相与测井参数相关矩阵图
Figure 5. Correlation matries of different lithofacies and logging parameters
图 6 测井参数对主成分的贡献图
Figure 6. Contribution of logging parameters for principal components
图 7 X5（a）、X6（b）井岩相识别结果
Figure 7. Lithofacies identification results for Wells X5(a), X6(b)
表 1 研究区页岩岩相类型发育特征
Table 1. Development characteristics of lithofacies types in the study area
岩相类型 高碳硅质 中碳混合质 中碳硅质 低碳黏土质 低碳混合质 矿物体积分数
φB/%
岩心照片 
铸体薄片 
扫描电镜 
w（TOC）/% 3.9（1.8～6.7） 2.8（1.9～4.5） 2.8（1.6～3.5） 1.5（0.4～2.9） 0.9（0.1～2.9） POR/% 3.9（2.6～5.5） 3.8（0.4～6.3） 3.4（2.9～4.3） 3.351（1.081～4.616） 1.8（0.1～4.6） φ（TGAS）/% 4.1（1.7～8.2） 3.9（2.7～6.4） 3.8（2.4～5.4） 2.2（0.8～5.1） 1.3（0.2～5.5） 总体评价 优质 较优等 中等 差 最差 注：POR为孔隙度；φ（TGAS）为总含气量 
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表 2 数据增强与样本集划分
Table 2. Data augmentation and sample division
页岩岩相 高碳硅质 高碳混合质 中碳硅质 中碳混合质 中碳黏土质 低碳硅质 低碳混合质 低碳黏土质 统计 原始数据 训练集数据/个 639 73 546 408 26 222 252 46 2212 2766 测试集数据/个 167 16 147 88 7 52 67 10 554 识别准确率/% 89 77 88 91 58 67 85 62 平均77 增强数据 训练集数据/个 700 700 700 700 700 700 700 700 5600 7200 测试集数据/个 200 200 200 200 200 200 200 200 1600 识别准确率/% 92 88 92 93 87 90 91 89 平均90
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表 3 随机森林算法参数调优
Table 3. Parameter tuning of the random forest algorithm
参数 搜索范围 步长 最优值 决策树的个数 200～ 2000 200 1000 决策树最大深度 10～100 10 100 叶子节点含有的最少样本 1～4 2 4 节点可分的最小样本数 1～10 1 2
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