Small-data-driven machine learning for well logging characterization of pore structure in low-permeability sandstone reservoirs

doi:10.7605/gdlxb.2026.049

Journal of Palaeogeography（Chinese Edition） ›› 2026, Vol. 28 ›› Issue (1): 369-380. doi: 10.7605/gdlxb.2026.049

• NEW TECHNOLOGY AND NEW METHODS • Previous Articles Next Articles

Small-data-driven machine learning for well logging characterization of pore structure in low-permeability sandstone reservoirs

SUN Yuxi¹(), QI Yuan¹, CHEN Liang², HE Yiping³, JI Hancheng¹(), SHI Yanqing¹, ZHAO Boyuan¹, LIUZHU Ruizhi⁴

1 College of Geosciences,China University of Petroleum(Beijing),Beijing 102249,China
2 College of Science,China University of Petroleum(Beijing),Beijing 102249,China
3 The Fifth Oil Production Plant,PetroChina Changqing Oilfield Company,Xi’an 710200,China
4 Oil and Gas Technology Research Institute,PetroChina Changqing Oilfield Company,Xi’an 710018,China

Received:2025-08-03 Revised:2025-10-21 Online:2026-02-01 Published:2026-02-09
Contact: JI Hancheng,born in 1966,is a professor and Ph.D. supervisor at China University of Petroleum(Beijing). He is mainly engaged in sedimentology and reservoir geology. E-mail: jhch@cup.edu.cn.
About author:
SUN Yuxi,born in 1997,is a Ph.D. candidate at China University of Petroleum(Beijing). He is mainly engaged in reservoir geology. E-mail: 2860485642@qq.com.
Supported by:
Science Foundation of China University of Petroleum(Beijing)(2462023BJR011); Research Project of PetroChina Changqing Oilfield Company(2023DJ0911)

基于小样本机器学习的低渗透砂岩储集层孔隙结构测井表征方法^*

孙玉玺¹(), 齐媛¹, 陈亮², 贺燚平³, 季汉成¹(), 史燕青¹, 赵博缘¹, 刘朱睿鸷⁴

1 中国石油大学(北京)地球科学学院,北京 102249
2 中国石油大学(北京)理学院,北京 102249
3 中国石油长庆油田公司第五采油厂,陕西西安 710200
4 中国石油长庆油田公司油气工艺研究院,陕西西安 710018

通讯作者: 季汉成,男,1966年生,中国石油大学(北京)教授,博士生导师,主要从事沉积学、储层地质学等研究。E-mail: jhch@cup.edu.cn。
作者简介:
孙玉玺,男,1997年生,中国石油大学(北京)博士研究生,主要从事储层地质学研究。E-mail: 2860485642@qq.com。
基金资助:
*中国石油大学(北京)科研启动基金(2462023BJR011); 中国石油长庆油田分公司科研项目(2023DJ0911)

Abstract

Abstract:

In recent years,machine learning has shown significant advantages in reservoir parameter evaluation based on well logs. However,due to the limited size of core samples,the generalization ability of these models is often not guaranteed. In this study,more than 100 sets of high-pressure mercury injection data and seven types of well logs were collected from the Triassic Yanchang Formation in the Jiyuan area of the Ordos Basin. The study systematically compares the performance of TabPFN and seven other common machine learning methods under small data conditions. The SHAP interpretability analysis is also used to understand the model’s decision-making mechanisms. The results show: (1)The average permeability of the Yanchang Formation sandstone reservoir in the study area is 0.39×10^-3 μm²,which is typical of low- to ultra-low permeability reservoirs. These reservoirs have small pore throat radii and complex pore structures. The first principal component explains 65.00% of the total variance of the pore structure parameters. Combined with production data and mechanism analysis,it is considered an appropriate target variable for characterizing pore structure. (2)The TabPFN model performs excellently in small-data environments,achieving R² values of 0.81 and 0.87 in the validation and blind well test sets,respectively. Its high performance without parameter tuning demonstrates its outstanding applicability. (3)According to feature importance ranking,density log,sedimentary facies,acoustic log,geological layering,and deep resistivity are the top five features. Sedimentary facies and geological layering make greater contributions in extreme pore structure intervals,highlighting the critical role of discrete geological variables in improving model performance. This study provides an effective data-driven case for characterizing the pore structure of low-permeability sandstone reservoirs under small data conditions.

Key words: TabPFN, machine learning, pore structure, low-permeability sandstone, tight sandstone, Yanchang Formation, Ordos Basin

摘要：

近年来,机器学习在基于测井数据的储集层参数评价中展现出显著优势,但受限于取心实验样本数量,模型的泛化能力往往无法保障。研究在鄂尔多斯盆地姬塬地区三叠系延长组收集了100余组高压压汞实验数据和7种常规测井数据,采用TabPFN和其他常见7种机器学习方法,系统地比较在小样本环境下不同方法的性能表现,并基于SHAP可解释性分析理解模型决策机制。研究表明: (1)研究区延长组砂岩储集层平均渗透率为0.39×10^-³ μm²,为典型的低渗—超低渗储集层,具有孔喉半径小、孔隙结构复杂等特点。第一主成分能解释孔隙结构参数65.00%的总方差,通过生产数据、机理分析等认为其可作为表征孔隙结构的目标变量。(2)TabPFN模型在小样本环境中表现优异,验证集和盲井测试集中R²分别为0.81和0.87。由于其无需调参即可实现较高的性能,展现出更为突出的适用性。(3)特征重要性排名中,密度测井、沉积相、声波测井、地质分层以及深电阻率为前5大特征。其中,沉积相与地质分层在极端孔隙结构区间贡献更为突出,提示离散地质变量对提升模型性能的关键作用。研究为在小样本环境下的低渗透砂岩储集层孔隙结构表征提供了有效的数据驱动案例。

关键词: TabPFN, 机器学习, 孔隙结构, 低渗透砂岩, 致密砂岩, 延长组, 鄂尔多斯盆地

CLC Number:

TE122

SUN Yuxi, QI Yuan, CHEN Liang, HE Yiping, JI Hancheng, SHI Yanqing, ZHAO Boyuan, LIUZHU Ruizhi. Small-data-driven machine learning for well logging characterization of pore structure in low-permeability sandstone reservoirs[J]. Journal of Palaeogeography（Chinese Edition）, 2026, 28(1): 369-380.

孙玉玺, 齐媛, 陈亮, 贺燚平, 季汉成, 史燕青, 赵博缘, 刘朱睿鸷. 基于小样本机器学习的低渗透砂岩储集层孔隙结构测井表征方法^*[J]. 古地理学报, 2026, 28(1): 369-380.

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URL: http://www.gdlxb.cn/EN/10.7605/gdlxb.2026.049

http://www.gdlxb.cn/EN/Y2026/V28/I1/369

Figures/Tables 11

Fig.1 Petrophysical characteristics of low-permeability sandstones in the Yanchang Formation,Jiyuan area, Ordos Basin

Fig.2 Clustered heatmap of the correlations between pore structure parameters revealed by HPMI and well logs

Fig.3 PCA of pore structure parameters revealed by HPMI

Fig.4 Comparative performance of various machine learning models in predicting the PC1

Fig.5 Taylor diagram showing the projections of various machine learning models

Table 1 Model optimization and training durations for various machine learning models

模型	寻优耗时/s	训练耗时/s	主要参数	寻优范围
TabPFN	/	0.11	/	/
RF	18.37	0.08	n_estimators;max_depth;max_features; min_samples_split;bootstrap	100~600;3~20;['sqrt','log2',0.5~1.0]; 2~20;[True,False]
XGBoost	19.57	0.07	n_estimators;max_depth;learning_rate; subsample;colsample_bytree	50~500;3~10;1e-3~0.3;0.5~1.0;0.5~1.0
LightGBM	11.09	0.03	num_leaves;learning_rate;n_estimators; feature_fraction;bagging_fraction	16~128;1e-3~0.3;50~500;0.5~1.0;0.5~1.0
DNN	11.21	0.10	n_layers;hidden;alpha;lr_init; activation;solver	1~3;16~256;1e-5~1e-1;1e-4~1e-1; ['relu','tanh'];['adam','sgd']
SVM	4.58	0.00	C;epsilon;gamma;kernel	1e-3~1e3;1e-4~1.0;['scale','auto']; ['rbf','poly','sigmoid']
MR	/	0.00	/	/
KNN	8.30	0.00	n_neighbors;weights;p	1~20;['uniform','distance'];1~2

Fig.6 Prediction curves of the three best-performing machine learning models

Fig.7 Correlation analysis between PC1 and selected variables

Fig.8 Correlation analysis between PC1 and pore structure parameters revealed by HPMI

Fig.9 Feature importance ranking based on SHAP values

Fig.10 Mean |SHAP| contributions of different features with respect to variations in PC1

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