Lithologic Reservoirs ›› 2026, Vol. 38 ›› Issue (3): 94-106.doi: 10.12108/yxyqc.20260308

• PETROLEUM EXPLORATION • Previous Articles     Next Articles

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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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

CLC Number: 

  • TE122

Fig. 1

Structural location of Hechuan area (a), sedimentary environment (b), and comprehensivestratigraphic column of Silurian Longmaxi Formation (c)， central Sichuan Basin"

Fig. 2

Ternary diagram of “mineral composition + TOC content” of shale from Silurian Longmaxi Formationin Hechuan area"

Fig. 3

Development characteristics of shale lithofacies of Silurian Longmaxi Formation in Hechuan area,central Sichuan Basin"

Fig. 4

Crossplot of shale lithofacies logging responses of Silurian Longmaxi Formation in Hechuan area, central Sichuan Basin"

Fig. 5

Flowchart of AdaBoost algorithm"

Fig. 6

Normalized box plot of logging responses for different shale lithofacies of Silurian Longmaxi Formation in Hechuan area, central Sichuan Basin"

Fig. 7

BSMOTE-sampled balanced data for shale lithofacies of Silurian Longmaxi Formation in Hechuan area, central Sichuan Basin"

Table 1

Parameter settings for shale lithofacies identification models of AdaBoost, RF, and GBDT of Silurian Longmaxi Formation in Hechuan area, central Sichuan Basin"

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

Fig. 8

AdaBoost parameters search and model errors for shale lithofacies identification of Silurian Longmaxi Formation in Hechuan area, central Sichuan Basin"

Table 2

Calculation formulas of evaluation parameters for shale lithofacies identification models of Silurian Longmaxi Formation in Hechuan area, central Sichuan Basin"

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

Fig. 9

Comparison of shale lithofacies prediction performance among multiple models of Silurian Longmaxi Formation in Hechuan area, central Sichuan Basin"

Fig. 10

Comparison of confusion matrices for shale lithofacies prediction among multiple models of Silurian Longmaxi Formation in Hechuan area, central Sichuan Basin"

Fig. 11

Cumulative principal component curve for TOC content prediction (a) and SVR model parameter optimization (b) of shale from Silurian Longmaxi Formation in Hechuan area, central Sichuan Basin"

Fig. 12

Comparison between PCA-SVR predicted TOC content and core-measured TOC content of shale from Silurian Longmaxi Formation in Hechuan area, central Sichuan Basin"

Table 3

Shale lithofacies performance predicted by combination of BSMOTE-AdaBoost and PCA-SVR of Silurian Longmaxi Formation of three blind wells in Hechuan area, central Sichuan Basin"

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

Fig. 13

Comparison of shale lithofacies identification results from multiple models of Silurian Longmaxi Formation in well T4, Hechuan area, central Sichuan Basin"

Fig. 14

Correlation of shale lithofacies well-tie of Silurian Longmaxi Formation in well T1—T4—T11 of Hechuan area, central Sichuan Basin"

Fig. 15

Prediction for shale lithofacies distribution of sublayer 4 of Long 11 submember in Silurian Longmaxi Formation, Hechuan area, central Sichuan Basin"

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