Identification of Shale Lithofacies by Well Logs Based on Random Forest Algorithm
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摘要: 泥页岩岩相识别是页岩油空间分布及勘探目标预测的一项重要工作，受地层非均质性及测井信息冗余的制约，基于测井响应方程的岩相识别十分困难.本文建立了一种基于随机森林算法的岩相识别模型，使用SHAP方法量化测井参数重要性.结果表明：随机森林算法可以很好地识别泥页岩岩相，其准确率高于支持向量机、KNN和XGBoost，并且对数据集中岩相类别不均衡的分类问题更加有效；对模型识别岩相最重要的前3项测井参数是自然电位、井径和声波时差；该模型可快速识别单井岩相，再根据总孔隙度、游离烃S1、TOC等参数可确定有利岩相类型，进而确定研究区有利岩相分布，为后续“甜点”预测提供依据.Abstract: Shale lithofacies identification is an important task in the spatial distribution of shale oil and exploration target prediction, but it is difficult to identify lithofacies based on logging response equations due to the formation heterogeneity and redundancy of logging information. In this paper, a lithofacies identification model based on random forest algorithm is proposed, which uses the SHAP method to quantify the contribution of logging parameters. The results show that the random forest algorithm can identify shale lithofacies well, and its accuracy is higher than support vector machine, k-nearest neighbors and XGBoost; SP, CAL and AC contribute the most to the model's identification of lithofacies. The model can quickly identify the lithofacies of a single well, and determine the favorable lithofacies by combining total porosity, free hydrocarbon S1, TOC, etc., and then determine the distribution of favorable lithofacies in the whole area, providing a basis for subsequent "sweet spot" prediction.
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Key words:
- random forest /
- machine learning /
- logging /
- lithofacies identification /
- shale
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图 1 松辽盆地某凹陷A段泥页岩矿物组成分布
Fig. 1. Mineral composition distribution of shale in Formation A of a depression in Songliao Basin
图 3 不同岩相与测井参数相关矩阵图
Fig. 3. Correlation matrix of different lithofacies and logging parameters
图 4 不同模型岩相识别结果对比
Fig. 4. Comparison of predicted results from different lithofacies identification models
图 5 不同岩相识别模型混淆矩阵
混淆矩阵中行代表真实岩相，每一行中的数据表示真实岩相被预测的岩相类别分布，如a图第二行中的0.95，表示95%的实测岩相为2的样品被预测为类别2；列代表预测岩相，每一列中的数据表示实测岩相被预测为该类的比例分布
Fig. 5. Comparison of confusion matrices for different lithofacies identification models
图 6 岩相识别中测井参数重要性分析
Fig. 6. Analysis of the importance of logging parameters in lithofacies identification
图 7 X井不同页岩岩相识别模型预测结果对比
Fig. 7. Comparison of predicted results from different lithofacies identification models for Well X
图 8 松辽盆地某凹陷A段有利岩相分布
Fig. 8. Distribution of favourable lithofacies in Formation A of a depression in the Songliao Basin
表 1 松辽盆地某凹陷A段岩相类型发育特征
Table 1. Characteristics of lithofacies type development in Formation A of a depression in the Songliao Basin
序号 岩相类型 岩心观察 薄片观察 TOC(%) 岩石构造 矿物组成(%) 1 富有机质
纹层状
黏土质页岩
> 2.0 页理发育，黏土纹层夹长英质/碳酸盐纹层 黏土 > 50，
长英质 < 50，
碳酸盐 < 502 富有机质
层状
黏土质页岩
> 2.0 页理发育，黏土层夹长英质/碳酸盐层 黏土 > 50，
长英质 < 50，
碳酸盐 < 503 中等有机质
纹层状黏土质页岩
1.0~ 2.0 页理发育，黏土纹层夹长英质/碳酸盐纹层 黏土 > 50，
长英质 < 50，
碳酸盐 < 504 富有机质
纹层状混合质页岩
> 2.0 页理发育，黏土质、长英质、碳酸盐纹层互层 黏土 < 50，
长英质 < 50，
碳酸盐 < 505 富有机质
层状混合质页岩
> 2.0 页理发育，黏土质、长英质、碳酸盐交互层 黏土 < 50，
长英质 < 50，
碳酸盐 < 506 低有机质
块状灰质
泥岩
< 1.0 无页理，碳酸盐矿物颗粒均匀分布 黏土 < 50，
长英质 < 50，
碳酸盐 > 50
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表 2 数据集中不同岩相样本分布
Table 2. The distribution of the different lithofacies samples in the dataset
岩相标签 岩相 样本数 占比（%） 1 富有机质纹层状混合质页岩 46 13.26 2 富有机质纹层状黏土质页岩 78 22.48 3 富有机质层状黏土质页岩 30 8.65 4 中等有机质纹层状黏土质页岩 26 7.49 5 富有机质层状混合质页岩 48 13.83 6 低有机质块状灰质泥岩 76 21.90 10 其他 43 12.39 统计 347
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表 3 随机森林算法参数调优
Table 3. Parameters tuning for random forest
参数 搜索范围 步长 最优值 迭代次数 100~500 2 120 最大树深度 1~15 1 10 内部节点再划分所需最小样本数 1~50 1 4 叶子结点最小样本数 1~20 1 1
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表 4 不同模型的岩相识别F1-score
Table 4. F1-score of different lithofacies identification models
岩相标签 岩相 KNN SVM XGBoost RF 1 富有机质纹层状混合质页岩 0.88 0.85 0.91 0.88 2 富有机质纹层状黏土质页岩 0.91 0.80 0.93 0.95 3 富有机质层状黏土质页岩 0.78 0.84 0.82 0.91 4 中等有机质纹层状黏土质页岩 0.63 0.82 0.78 0.82 5 富有机质层状混合质页岩 0.86 0.70 1.00 1.00 6 低有机质块状灰质泥岩 0.79 0.82 0.87 0.89 10 其他 0.67 0.81 0.86 0.86
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