Lithologic Reservoirs ›› 2026, Vol. 38 ›› Issue (1): 55-66.doi: 10.12108/yxyqc.20260105

• PETROLEUM EXPLORATION • Previous Articles     Next Articles

Evaluation method for oil saturation in low resistivity reservoirs based on random forest optimization algorithm

YIN Jiang1(), JIAO Xuejun2, LI Xiaolong3, LI Taifu4, SHEN Zhanyong4, LI Mengxi5, SUN Rui1, ZHU Yushuang1()   

  1. 1 State Key Laboratory of Continental Evolution and Early Life/Department of Geology, Northwest University, Xi’an 710069, China
    2 China National Petroleum Corporation Shared Services Co., Ltd., Xi’an Center, Xi’an 710069, China
    3 Well Operation Engineering Company, Daqing Drilling Engineering Co., Ltd.， Songyuan 138000, Jilin, China
    4 Oil Production Plant No. 7, PetroChina Changqing Oilfield Company, Xi’an 710018, China
    5 Huanqing Company, PetroChina Yumen Oilfield, Qingyang 745799, Gansu, China
  • Received:2025-03-23 Revised:2025-08-05 Online:2026-01-01 Published:2026-01-23
  • Contact: ZHU Yushuang E-mail:yjyj0068@163.com;zysnwu@163.com

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Abstract:

To address the challenges in interpreting oil saturation in low resistivity reservoirs using conventional electrical logging methods, an evaluation method based on a random forest optimization algorithm was proposed, and was applied in low resistivity reservoirs of Jurassic Yan’an Formation, Huanqing area, Ordos Basin. The results show that: (1) An oil saturation interpretation model was established using random forest regression algorithm, the Nutcracker Optimizer Algorithm (NOA) was introduced to optimize the hyperparameter tuning of the random forest. By integrating core and logging data through machine learning training, an NOA-optimized random forest saturation model (NOA-RF) was established. (2) NOA method accelerates the training speed of the random forest model, it takes 26.17 minutes to identify the globally optimal hyperparameter combination,which is 36.18 minutes faster than conventional grid search. It also improves the fitting accuracy of the oil saturation model by 96.6%, outperforming grid search by 83.9% and Archie’s method by 45.2%. (3) NOA-RF model achieved a correlation coefficient up to 0.977 9 between predicted and core actual oil saturation in Huanqing area low resistivity reservoirs, with oil-water layer identification accuracy of 93.33%, representing 53.33% improvement over Archie’s method.

Key words: low resistivity reservoir, nutcracker optimizer algorithm, random forest regression algorithm, oil saturation, Yan’an Formation, Jurassic, Huanqing area, Ordos Basin

CLC Number: 

  • TE122

Fig. 1

Structural location of Huanqing area (a) and stratigraphic column of Jurassic Yan’an Formation (b), Ordos Basin"

Table 1

Statistics of clay mineral and pyrite content of Jurassic Yan’an Formation reservoir, Huanqing area"

样
号		 w（黏土矿物）/% w（黄铁矿）/%
高岭石 伊利石 绿泥石 合计
Y1 1.67 2.81 0.96 5.44 0.43
Y2 1.07 2.2 0.67 3.94 0.66
Y3 0.95 2.09 0.92 3.96 0.41
Y4 1.36 2.14 1.21 4.71 0.72
Y5 2.24 3.38 1.23 6.85 0.58
Y6 0.96 1.98 1.51 4.45 0.53
Y7 1.45 2.48 1.06 4.99 0.65
Y8 1.86 3.01 1.18 6.05 0.48
Y9 1.14 2.22 1.35 4.71 0.55
Y10 1.51 2.68 0.83 5.02 0.68
Y11 0.89 2.34 1.42 4.65 0.52
Y12 1.78 2.57 0.73 5.08 0.47

Fig. 2

Logging response characteristics and oil saturation prediction performance of electrical models of Jurassic Yan’an Formation in well Q-n25, Huanqing area"

Fig. 3

Workflow of random forest algorithm"

Fig. 4

Crossplot of acoustic time difference and wellbore diameter of Jurassic Yan’an Formation in well M-7J, Huanqing area"

Table 2

Importance and ranking of oil saturation influencing factors based on random forest algorithm"

特征因素 重要性 排序 特征因素 重要性 排序
AT90 0.531 1 SP 0.175 7
AC 0.405 2 DEN 0.152 8
AT60 0.353 3 AT10 0.147 9
AT30 0.317 4 GR 0.113 10
CNL 0.295 5 PE 0.082 11
AT20 0.231 6

Fig. 5

Correlation matrix of oil saturation influencing factors based on Pearson correlation coefficient analysis"

Fig. 6

Feature importance ranking of input parameters in random forest regression"

Fig. 7

Convergence behavior of the Nutcracker Optimizer Algorithm (NOA)"

Fig. 8

Optimization effect of hyperparameters in grid search model"

Table 3

Comparison of hyperparameter optimization effects between two random forest models"

寻优方法 超参数 寻优范围 最优取值
NOA法 n_estimators [1,500] 267
max_depth [5,30] 11
max_features [auto] 10
网格搜索法 max_depth [1,500] 391
max_depth [5,30] 7
max_features [auto] 10

Table 4

Comparison of performance evaluation of three oil saturation prediction models"

模型 数据集 R2 平均绝对误差 平均相对
误差/%		 均方
误差		 均方根误差
Archie法 训练集 0.476 29.863 45.93 894.727 29.912
测试集 0.452 30.375 47.55 929.823 30.493
网格搜索
随机森林
算法		 训练集 0.871 6.932 10.71 47.775 6.912
测试集 0.839 8.261 12.62 65.496 8.093
NOA-RF 训练集 0.967 2.125 3.70 5.817 2.412
测试集 0.966 2.351 4.44 8.105 2.847

Fig. 9

Performance metrics of NOA-optimized random forest model"

Fig. 10

Oil saturation prediction based on NOA-optimized random forest of Jurassic Yan’an Formation in well Q-n32, Huanqing area"

Fig. 11

Oil saturation prediction results based on NOA-optimized random forest of Jurassic Yan’an Formation in well Q-62, Huanqing area"

Fig. 12

Prediction accuracy of oil saturation based on NOA-optimized random forest of Jurassic Yan’an Formation in two low resistivity wells of Huanqing area"

Table 5

Comparison of reservoir evaluation effectiveness between NOA-RF method and Archie’s method for low resistivity reservoirs of Jurassic Yan’an Formation， Huanqing area"

井名 解释
层号		 NOA-RF法 Archie法 试油
结论
含油饱
和度/%		 解释
结论		 含油饱
和度/%		 解释
结论
Q-n2 19 63.27 油层 42.51 油水同层 油层
Q-n5 26 41.73 油水同层 27.82 水层 油水同层
Q-n7 18 57.38 油层 39.66 油水同层 油层
M-3J 23 29.26 水层 23.19 水层 水层
M-6J 20 9.27 水层 31.63 油水同层 水层
M-7J 17 5.26 水层 19.34 水层 水层
M-80 15 68.02 油层 50.72 油层 油层
M-82 21 12.51 水层 27.35 水层 水层
L-2J 32 58.92 油层 38.57 油水同层 油层
L-3J 35 15.29 水层 28.76 水层 水层
L-4J 29 32.35 油水同层 30.29 油水同层 水层
L-5J 41 53.28 油层 39.83 油水同层 油层
K-1J 38 55.67 油层 34.39 油水同层 油层
K-5J 25 10.36 水层 26.35 水层 水层
K-6J 36 50.31 油层 32.65 油水同层 油层
准确率/% 93.33 40.00
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