Lithologic Reservoirs ›› 2026, Vol. 38 ›› Issue (2): 111-121.doi: 10.12108/yxyqc.20260210

• PETROLEUM EXPLORATION • Previous Articles     Next Articles

Intelligent identification of fluid logging in tight sandstone reservoirs based on GWO-XGBoost model: A case study of Triassic Chang 8 member in Hongde area, Ordos Basin

XUE Bowen1,2(), ZHANG Zhaohui1,2(), ZHANG Jiaosheng3, ZOU Jiandong3, ZHANG Wenting4   

  1. 1 Xinjiang Key Laboratory for Geodynamic Processes and Metallogenic Prognosis of the Central Asian Orogenic Belt, Xinjiang University, Urumqi 830017, China
    2 Collaborative Innovation Center of Green Mining and Ecological Restoration for Xinjiang Mineral Resources, Urumqi 830046, China
    3 Research Institute of Exploration and Development, PetroChina Changqing Oilfield Company, Xi’an 710018, China
    4 PetroChina Research Institute of Petroleum Exploration and Development-Northwest, Lanzhou 730020, China
  • Received:2025-09-01 Revised:2025-10-15 Online:2026-03-01 Published:2025-12-15

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Abstract:

Traditional logging interpretation methods exhibit low accuracy in identifying fluid properties in tight sandstone reservoirs, in response to such problem, an intelligent identification method for reservoir fluid based on GWO-XGBoost model was proposed, and it was applied to Triassic Chang 8 tight sandstone reservoirs in Hongde area of Ordos Basin. The results show that: （1） Taking actual well testing data of Triassic Chang 8 tight sandstone reservoir in Hongde area of Ordos Basin as the target variable, nine logging curves, including acoustic, spontaneous potential, density, caliper, neutron, natural gamma, and three resistivity logs (AT 20, AT60, and AT90)，were selected as features parameters through principal component analysis. Then, key hyperpara-meters of XGBoost model were globally optimized using grey wolf optimization (GWO) algorithm. （2） GWO-XGBoost model has an accuracy of 96.55% in identifying reservoir fluid types, which is significantly higher than XGBoost, Random Forest (RF), and Support Vector Machine (SVM) models by 6.03%, 6.89%, and 22.41%, respectively. （3） In practical single-well applications, GWO-XGBoost model, through comprehensive analysis and nonlinear feature learning of multi-dimensional logging responses, effectively overcomes the common misclassification between low-resistivity oil layers and high-resistivity water layers in manual interpretation. This model exhibits high stability and reliability under complex reservoir conditions, providing effective technical support for improving the efficiency of tight sandstone oil and gas exploration and development.

Key words: XGBoost, grey wolf optimization algorithm （GWO）, intelligent model, reservoir fluid identification, tight sandstone, unconventional oil and gas, Triassic, Hongde area, Ordos Basin

CLC Number: 

  • TE121.1

Fig. 1

Distribution of sedimentary facies of Hongde area (a) and comprehensive stratigraphic column of the 8th member of Triassic Yanchang Formation (b), Ordos Basin"

Table 1

Statistics of correlation indicators between well logging and fluid types of Triassic Chang 8 member in Hongde area, Ordos Basin"

名称 PC1 PC2 PC3 综合得分系数 权重/%
AC
 -0.197 580
 -0.136 170
 0.689 269
 -0.014 130
 14.18
AT 20 0.512 187 0.149 821 -0.009 810 0.243 170 10.37
AT60 0.520 014 0.130 652 0.023 080 0.244 804 10.43
AT 90 0.519 098 0.132 014 0.028 915 0.245 814 10.42
CAL 0.031 978 -0.465 760 0.080 078 -0.131 500 9.21
CNL -0.074 120 0.469 021 0.447 027 0.198 049 12.67
DEN -0.072 650 0.527 434 0.269 880 0.190 799 11.59
GR 0.188 145 -0.393 310 0.424 755 0.005 334 11.81
SP 0.334 004 -0.233 940 0.252 875 0.087 547 9.32

Fig. 2

Violin plots of well logging parameters for four types of fluids in Triassic Chang 8 member in Hongde area, Ordos Basin"

Table 2

Fluid types, labels, and samples of Triassic Chang 8 member in Hongde area, Ordos Basin"

流体类型 标签 样本数 样本占比/%
水层 0 66 10.43
含油水层 1 64 10.11
油水同层 2 277 43.76
油层 3 226 35.70

Fig. 3

XGBoost training flowchart"

Fig. 4

Hierarchical system of grey wolf algorithm"

Fig. 5

Schematic diagram of grey wolf position update in GWO algorithm"

Fig. 6

Flowchart of GWO-XGBoost fluid intelligent identification"

Fig. 7

Relationship between iteration error and iteration times of GWO-XGBoost model"

Fig. 8

Confusion matrix heatmap of GWO-XGBoost model"

Fig. 9

Comparison of true values of four models with predicted results"

Fig. 10

Summary diagram of evaluation indicators for reservoir fluid identification performance predicted by four models"

Fig. 11

Comparison of predicted results of reservoir fluid from well logging interpretation and GWO-XGBoost model of Triassic Chang 8 member in well DE41 (a) and well DE24 (b) in Hongde area, Ordos Basin"

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