岩性油气藏 ›› 2026, Vol. 38 ›› Issue (3): 94–106.doi: 10.12108/yxyqc.20260308

• 地质勘探 • 上一篇    下一篇

川中合川地区志留系龙马溪组页岩岩相测井智能识别方法

刘若彤(), 张大智, 隋立伟(), 肖利梅, 田亚, 孙山, 彭丹丹, 李建智   

  1. 中国石油大庆油田有限责任公司 勘探开发研究院， 黑龙江 大庆 163712
  • 收稿日期:2025-12-08 修回日期:2026-01-13 出版日期:2026-05-01 发布日期:2026-02-11
  • 第一作者:刘若彤（1996—），女，硕士，工程师，主要从事基础地质、风险勘探工作。地址：（163712）黑龙江省大庆市让胡路区西灵路18号。Email:liuruott1996@163.com。
  • 通信作者: 隋立伟（1986—），男，硕士，高级工程师，主要从事风险勘探、构造地质学工作。Email:suiliwei@petrochina.com.cn。
  • 基金资助:
    中国石油天然气股份有限公司科技重大专项“四川盆地风险勘探领域和目标研究、工程技术攻关及现场试验”(2023YQX10104)

Intelligent logging identification method for shale lithofacies of Silurian Longmaxi Formation in Hechuan area, central Sichuan Basin

LIU Ruotong(), ZHANG Dazhi, SUI Liwei(), XIAO Limei, TIAN Ya, SUN Shan, PENG Dandan, LI Jianzhi   

  1. Exploration and Development Research Institute, PetroChina Daqing Oilfield Co., Ltd.， Daqing 163712，Heilongjiang， China
  • Received:2025-12-08 Revised:2026-01-13 Online:2026-05-01 Published:2026-02-11

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摘要：

川中合川地区志留系龙马溪组页岩分布广，烃源岩品质好，是页岩气勘探的重点层系。基于岩心实验分析、测井资料，根据矿物组分和TOC含量对岩相进行了划分和测井智能识别，明确了龙马溪组岩相类别及空间展布特征，并划分了岩相有利区。研究结果表明：①合川地区志留系龙马溪组发育7种页岩岩相，以富、贫有机质长英质页岩岩相，富、贫有机质黏土质长英质混合页岩岩相、贫有机质黏土质页岩岩相为主，发育少量富有机质混合质页岩岩相、富有机质钙质长英质混合页岩岩相；其中，富有机质长英质页岩岩相、富有机质混合质页岩岩相、富有机质钙质长英质混合页岩岩相为勘探有利岩相。②测井智能识别方法为，引入BSMOTE算法均衡少数类岩相样本，构建集成学习模型实现矿物组分一级划分，基于主成分分析的支持向量机（PCA-SVR）模型预测TOC含量实现二级划分；BSMOTE-AdaBoost为岩相一级划分最优分类模型，PCA-SVR模型在岩相二级划分检验精度高，盲井验证岩相识别技术符合率达86.7%，区域适用性强。③合川地区志留系龙马溪组页岩相展布受古水深与物源供给控制，岩相纵向上呈现自下而上富有机质长英质页岩岩相向贫有机质混合页岩岩相演化趋势；平面上，有利岩相由NW—SE方向迁移；龙一11小层富有机质长英质岩相连续分布区是勘探关键目标。

关键词: 页岩气, 测井识别, 机器学习, BSMOTE-AdaBoost, 支持向量机, 龙马溪组, 志留系, 合川地区, 四川盆地

Abstract:

Silurian Longmaxi Formation shale in Hechuan area of central Sichuan Basin is widely distributed, with good quality source rock, which makes it a key target for shale gas exploration. Based on core experiment analysis and logging data, lithofacies were classified and logging intelligently was identified through mineral composition and TOC content. Lithofacies types and spatial distribution characteristics of Longmaxi Formation were clarified, and favorable lithofacies zones were delineated. The results show that: (1) Longmaxi Formation of Hechuan area in central Sichuan Basin has developed seven types of shale lithofacies, primarily dominated by organic-rich and organic-poor felsic shale lithofacies, organic-rich and organic-poor argillaceous-felsic mixed shale lithofacies, and organic-poor argillaceous shale lithofacies, with minor development of organic-rich mixed shale lithofacies and organic-rich calcareous-felsic mixed shale lithofacies. Among them, the organic-rich felsic shale lithofacies, organic-rich mixed shale lithofacies, and organic-rich calcareous-felsic mixed shale lithofacies are favorable targets for exploration. (2) The intelligent logging identification method involves introducing BSMOTE algorithm to balance minority lithofacies samples, constructing an ensemble learning model to achieve primary classification of mineral composition, and using a Principal Component Analysis-based Support Vector Regression (PCA-SVR) model to predict TOC content for secondary classification. BSMOTE-AdaBoost model was identified as the optimal model for primary lithofacies classification, while PCA-SVR model demonstrated high accuracy in secondary classification. Blind well verification confirmed an overall lithofacies identification accuracy of 86.7%, indicating strong regional applicability. (3) Shale lithofacies distribution of Longmaxi Formation in Hechuan area is controlled by paleo-water depth and provenance supply. Vertically, the lithofacies exhibit an evolutionary trend from organic-rich felsic shale facies to organic-poor mixed shale facies from the bottom towards the top. On the plane, favorable lithofacies migrate from NW to SE. The continuous distribution zone of organic-rich felsic facies in Long 11¹ sublayer represents the key target for exploration.

Key words: shale gas, logging identification, machine learning, BSMOTE-AdaBoost, support vector machine, Longmaxi Formation, Silurian, Hechuan area, Sichuan Basin

中图分类号: 

  • TE122

图1

川中合川地区构造位置(a)（据文献[5]修改）、沉积环境(b)及志留系龙马溪组岩性地层综合柱状图(c)"

图2

合川地区志留系龙马溪组页岩“矿物组分+TOC含量”三端元图版"

图3

川中合川地区志留系龙马溪组页岩岩相发育特征 注：图中数据均为最小值~最大值/平均值。"

图4

川中合川地区志留系龙马溪组页岩岩相测井响应交会图"

图5

AdaBoost算法流程图"

图6

川中合川地区志留系龙马溪组各页岩岩相测井响应归一化箱线图"

图7

川中合川地区志留系龙马溪组页岩样本采用BSMOTE过采样均衡处理效果"

表1

川中合川地区志留系龙马溪组3种页岩岩相识别模型参数设置"

模型 参数 搜索范围 最优
参数值
AdaBoost 弱分类器 决策树、逻辑回归、SVM 决策树
分类器数量 40~120 110
学习率 0.1~0.7 0.6
RF 树的数量 50~200 150
树的最大深度 3~15 10
叶节点最小样本数 3~10 3
GBDT 树的数量 40~120 80
树的最大深度 3~15 5
学习率 0.1~0.7 0.3

图8

川中合川地区志留系龙马溪组AdaBoost页岩岩相识别模型参数搜索与模型误差"

表2

川中合川地区志留系龙马溪组页岩岩相识别模型评价参数计算公式"

评价参数 公式 意义
准确率（GA） ${G}_{A} = \frac{TP +TN}{TP + TN + FP + FN}$ 整体的正确率，即正类、负类预测正确的比例
精确率(GP) ${G}_{P} = \frac{TP}{TP + FP}$ 预测为正类实际也为正类的比例
召回率(GR) ${G}_{R} = \frac{TP}{TP + FN}$ 实际正类中被正确预测的比例
F1 $F1 = \frac{2{G}_{P}{G}_{R}}{{G}_{P} + {G}_{R}}$ 同时考虑精确率、召回率的综合指标

图9

川中合川地区志留系龙马溪组多模型页岩岩相预测效果对比"

图10

川中合川地区志留系龙马溪组多模型页岩岩相预测混淆矩阵对比"

图11

川中合川地区志留系龙马溪组页岩TOC含量预测主成分累加曲线（a）及SVR模型参数优化（b）"

图12

川中合川地区志留系龙马溪组PCA-SVR模型预测的TOC含量与岩心测试TOC含量对比"

表3

川中合川地区3口盲井志留系龙马溪组BSMOTE-AdaBoost、PCA-SVR模型组合预测页岩岩相效果"

井名 岩心标定岩相/个 模型组合识别正确岩相/个 准确率/%
T1 21 18 85.7
T4 28 25 89.3
T11 11 9 81.8

图13

川中合川地区T4井志留系龙马溪组多模型页岩岩相识别效果对比"

图14

川中合川地区T1—T4—T11井志留系龙马溪组页岩岩相连井剖面（剖面位置见图1b）"

图15

川中合川地区志留系龙马溪组龙一1亚段4小层页岩岩相分布预测"

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