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<sup>*</sup>

doi:10.7605/gdlxb.2026.049

古地理学报 ›› 2026, Vol. 28 ›› Issue (1): 369-380. doi: 10.7605/gdlxb.2026.049

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

收稿日期:2025-08-03 修回日期:2025-10-21 出版日期:2026-02-01 发布日期:2026-02-09
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)

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)

摘要/Abstract

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

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

中图分类号:

TE122

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.

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.

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

图/表 11

图 1 鄂尔多斯盆地姬塬地区延长组低渗透砂岩物性特征 a—孔隙度与渗透率关系; b—毛细管压力曲线

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

图 2 高压压汞揭示的孔隙结构参数与测井数据相关性聚类热图

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

图 3 基于高压压汞实验的孔隙结构参数主成分分析 a—孔隙结构参数在主成分空间的投影; b—孔隙结构参数主成分方差贡献图

Fig.3 PCA of pore structure parameters revealed by HPMI

图 4 不同机器学习方法在PC1预测中的表现 a—TabPFN模型;b—RF模型;c—XGBoost模型;d—LightGBM模型;e—DNN模型;f—SVM模型;g—MR模型;h—KNN模型

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

图 5 不同机器学习模型在泰勒图上的投影 a—验证集; b—测试集

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

表 1 不同机器学习模型的寻优和训练耗时

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

图 6 3种表现较好的机器学习模型预测曲线

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

图 7 PC1与一些变量的相关性分析 a—投产初期产油量与PC1的相关性; b—砂岩厚度与PC1的相关性; c—泥岩厚度与PC1的相关性

Fig.7 Correlation analysis between PC1 and selected variables

图 8 PC1与高压压汞揭示的孔隙结构参数间的相关性分析 a—孔隙度; b—渗透率; c—中值压力; d—排驱压力; e—平均孔喉半径; f—分选系数; g—最大汞饱和度; h—储集层质量指数

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

图 9 基于SHAP的特征重要性排列

Fig.9 Feature importance ranking based on SHAP values

图 10 不同特征随PC1变化的平均|SHAP|贡献 a—绝对平均; b—相对平均

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

参考文献 50

[1]	邓秀芹, 程党性, 周新平, 时孜伟, 郭懿萱. 2020. 鄂尔多斯盆地下侏罗统地层水化学特征及成因分析. 沉积学报, 38(5): 1099-1110.
	[Deng X Q, Cheng D X, Zhou X P, Shi Z W, Guo Y X. 2020. Formation hydrochemical characteristics and genesis of the lower Jurassic,Ordos Basin. Acta Sedimentologica Sinica, 38(5): 1099-1110]
[2]	赖锦, 李红斌, 张梅, 白梅梅, 赵仪迪, 范旗轩, 庞小娇, 王贵文. 2023. 非常规油气时代测井地质学研究进展. 古地理学报, 25(5): 1118-1138. doi: 10.7605/gdlxb.2023.05.057
	[Lai J, Li H B, Zhang M, Bai M M, Zhao Y D, Fan Q X, Pang X J, Wang G W. 2023. Advances in well logging geology in the era of unconventional hydrocarbon resources. Journal of Palaeogeography(Chinese Edition), 25(5): 1118-1138]
[3]	刘爱疆, 左烈, 李景景, 李瑞, 张玮. 2013. 主成分分析法在碳酸盐岩岩性识别中的应用: 以地区寒武系碳酸盐岩储层为例. 石油与天然气地质, 34(2): 192-196.
	[Liu A J, Zuo L, Li J J, Li R, Zhang W. 2013. Application of principal component analysis in carbonate lithology identification: a case study of the Cambrian carbonate reservoir in YH field. Oil & Gas Geology, 34(2): 192-196]
[4]	吕奇奇, 辛红刚, 王林, 罗顺社, 淡卫东, 冯胜斌. 2023. 鄂尔多斯盆地宁县地区三叠系延长组7段湖盆细粒重力流沉积类型、特征及模式. 古地理学报, 25(4): 823-840. doi: 10.7605/gdlxb.2023.04.061
	[Lü Q Q, Xin H G, Wang L, Luo S S, Dan W D, Feng S B. 2023. Sedimentary types,characteristics and model of lacustrine fine-grained gravity flow in the Member 7 of Trassic Yanchang Formation in Ningxian area,Ordos Basin. Journal of Palaeogeography(Chinese Edition), 25(4): 823-840]
[5]	闵超, 文国权, 李小刚, 赵大志, 李昆成. 2024. 可解释机器学习在油气领域人工智能中的研究进展与应用展望. 天然气工业, 44(9): 114-126.
	[Min C, Wen G Q, Li X G, Zhao D Z, Li K C. 2024. Research progress and application prospect of interpretable machine learning in artificial intelligence in oil and gas industry. Natural Gas Industry, 44(9): 114-126]
[6]	唐佰强, 孟庆涛, 杨亮, 胡菲, 谭悦, 邢济麟, 刘招君, 张恩威, 董秦玮. 2025. 基于BSMOTE-SVM的细粒沉积岩岩相智能预测: 以松辽盆地青山口组一段为例. 古地理学报, 27(4): 937-949.
	[Tang B Q, Meng Q T, Yang L, Hu F, Tan Y, Xing J L, Liu Z J, Zhang E W, Dong Q W. 2025. Intelligent prediction of fine-grained sedimentary lithofacies based on BSMOTE-SVM: a case study of the Member 1 of Qingshankou Formation in Songliao Basin. Journal of Palaeogeography(Chinese Edition), 27(4): 937-949]
[7]	王鑫, 曾溅辉, 贾昆昆, 王伟庆, 李博, 安丛, 赵文. 2023. 成岩作用控制下低渗透砂岩润湿性演化过程及机制: 以渤海湾盆地东营凹陷为例. 石油与天然气地质, 44(5): 1308-1320.
	[Wang X, Zeng J H, Jia K K, Wang Q W, Li B, An C, Zhao W. 2023. Evolutionary process of the wet tability of low-permeability sandstone reservoirs under the control of diagenesis and its mechanism: a case study of the Dongying sag,Bohai Bay Basin. Oil & Gas Geology, 44(5): 1308-1320]
[8]	尹泽, 刘自亮, 彭楠, 沈芳, 谭梦琪, 王玉冲, 梁雨晨, 王红伟. 2019. 鄂尔多斯盆地西缘上三叠统延长组沉积相特征研究. 沉积学报, 37(1): 163-176.
	[Yin Z, Liu Z L, Peng N, Shen F, Tan M Q, Wang Y C, Liang Y C, Wang H W. 2019. Study on sedimentary facies features of the upper Triassic Yanchang Formation,in the western margin,Ordos Basin. Acta Sedimentologica Sinica, 37(1): 163-176]
[9]	张宪国, 刘玉从, 林承焰, 张涛, 黄鑫, 段冬平. 2019. 低渗—致密气层渗透率核磁测井解释方法. 中国矿业大学学报, 48(6): 1266-1275.
	[Zhang X G, Liu Y C, Lin C Y, Zhang T, Huang X, Duan D P. 2019. Permeability interpretation of low permeability and tight gas reservoirs with nuclear magnetic resonance logging. Journal of China University of Mining & Technology, 48(6): 1266-1275]
[10]	赵凯琳, 靳小龙, 王元卓. 2021. 小样本学习研究综述. 软件学报, 32(2): 349-369.
	[Zhao K L, Jin X L, Wang Y Z. 2021. Survey on few-shot learning. Journal of Software, 32(2): 349-369]
[11]	周锦涛, 余文超, 杜远生, 邓旭升, 翁申富, 雷志远. 2025. 中国含铝岩系中稀土元素的赋存形式: 主量元素数据驱动的集成机器学习分析. 古地理学报, 27(2): 307-320. doi: 10.7605/gdlxb.2025.045
	[Zhou J T, Yu W C, Du Y S, Deng X S, Weng S F, Lei Z Y. 2025. Occurrence forms of rare earth elements in aluminum-bearing rock series in China: ensemble machine learning analysis driven by major element data. Journal of Palaeogeography(Chinese Edition), 27(2): 307-320]
[12]	朱石磊. 2019. 鄂尔多斯盆地姬塬地区三叠系延长组低孔低渗透砂岩储层特征及控制因素研究. 中国地质大学(北京)博士学位论文: 67-83.
	[Zhu S L. 2019. Reservoir characteristics and controlling factors analyzing of low porosity,low permeability sand reservoir,Jiyuan in Ordos. Doctoral dissertation of China University of Geosciences(Beijing): 67-83]
[13]	Breiman L. 2001. Random forests. Machine Learning, 45(1): 5-32. doi: 10.1023/A:1010933404324
[14]	Cao L, Jiang F J, Chen Z X, Gao Y, Huo L, Chen D. 2025. Data-driven interpretable machine learning for prediction of porosity and permeability of tight sandstone reservoir. Advances in Geo-Energy Research, 16(1): 21-35. doi: 10.46690/ager URL
[15]	Chen T Q, Guestrin C. 2016. XGBoost:a scalable tree boosting system. In: The 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: Association for Computing Machinery,785-794.
[16]	Coker A K. 1995. Fortran Programs for Chemical Process Design,Analysis,and Simulation. Houston: Gulf Professional Publishing,1-102.
[17]	Feng R M, Chen S N, Bryant S, Liu J. 2019. Stress-dependent permeability measurement techniques for unconventional gas reservoirs: review, evaluation,and application. Fuel, 256: 115987. doi: 10.1016/j.fuel.2019.115987 URL
[18]	Gao H, Li H Z. 2015. Determination of movable fluid percentage and movable fluid porosity in ultra-low permeability sandstone using nuclear magnetic resonance(NMR)technique. Journal of Petroleum Science and Engineering, 133: 258-267. doi: 10.1016/j.petrol.2015.06.017 URL
[19]	He T, An L J, Chen P S, Chen J Z, Feng J S, Bzdok D, Holmes A J, Eickhoff S B, Yeo B T T. 2022. Meta-matching as a simple framework to translate phenotypic predictive models from big to small data. Nature Neuroscience, 25(6): 795-804. doi: 10.1038/s41593-022-01059-9 pmid: 35578132
[20]	Hollmann N, Müller S, Purucker L, Krishnakumar A, Körfer M, Hoo S B, Schirrmeister R T, Hutter F. 2025. Accurate predictions on small data with a tabular foundation model. Nature, 637(8045): 319-326. doi: 10.1038/s41586-024-08328-6
[21]	Iraji S, Soltanmohammadi R, Matheus G F, Basso M, Vidal A C. 2023. Application of unsupervised learning and deep learning for rock type prediction and petrophysical characterization using multi-scale data. Geoenergy Science and Engineering, 230: 212241. doi: 10.1016/j.geoen.2023.212241 URL
[22]	Ke G L, Meng Q, Finley T, Wang T F, Chen W, Ma W D, Ye Q W, Liu T Y. 2017. LightGBM:a highly efficient gradient boosting decision tree. In: The 31st International Conference on Neural Information Processing Systems. New York: Curran Associates Inc.,3149-3157.
[23]	Lai J, Wang G W, Wang S, Cao J T, Li M, Pang X J, Zhou Z L, Fan X Q, Dai Q Q, Yang L, He Z B, Qin Z Q. 2018a. Review of diagenetic facies in tight sandstones: diagenesis,diagenetic minerals,and prediction via well logs. Earth-Science Reviews, 185: 234-258. doi: 10.1016/j.earscirev.2018.06.009 URL
[24]	Lai J, Wang G W, Wang Z Y, Chen J, Pang X J, Wang S C, Zhou Z L, He Z B, Qin Z Q, Fan X Q. 2018b. A review on pore structure characterization in tight sandstones. Earth-Science Reviews, 177: 436-457. doi: 10.1016/j.earscirev.2017.12.003 URL
[25]	Lai J, Wang G W, Fan Q X, Pang X J, Li H B, Zhao F, Li Y H, Zhao X, Zhao Y D, Huang Y Y, Bao M, Qin Z Q, Wang Q Q. 2022. Geophysical well-log evaluation in the era of unconventional hydrocarbon resources: a review on current status and prospects. Surveys in Geophysics, 43(3): 913-957. doi: 10.1007/s10712-022-09705-4
[26]	Lai D D, Demartino C, Xiao Y. 2024a. Probabilistic machine leaning models for predicting the maximum displacements of concrete-filled steel tubular columns subjected to lateral impact loading. Engineering Applications of Artificial Intelligence, 135: 108704. doi: 10.1016/j.engappai.2024.108704 URL
[27]	Lai J, Su Y, Xiao L, Zhao F, Bai T Y, Li Y H, Li H B, Huang Y Y, Wang G W, Qin Z Q. 2024b. Application of geophysical well logs in solving geologic issues: past,present and future prospect. Geoscience Frontiers, 15(3): 101779.
[28]	Lecun Y, Bengio Y, Hinton G. 2015. Deep learning. Nature, 521(7553): 436-444. doi: 10.1038/nature14539
[29]	Li P K, Tong X R, Wang Y J, Zhang Q. 2025a. Meta doubly robust: debiasing CVR prediction via meta-learning with a small amount of unbiased data. Knowledge-Based Systems, 310: 112898. doi: 10.1016/j.knosys.2024.112898 URL
[30]	Li Y R, Liu B, Hu Z M, Cai C H, Zhang Q X, Guo J S, Zeng S T, Li F. 2025b. Conductive characterization of shale reservoir induced by pore fluids: a case study of longmaxi formation in Southern Sichuan region. Fuel, 387: 134412. doi: 10.1016/j.fuel.2025.134412 URL
[31]	Lu H, Yue D L, Jones S J, Li S X, Wang W R, Bai B, Hou X L, Li Z, Wu S G, Li Q. 2024. Lithofacies assemblage and effects on diagenesis in lacustrine tight sandstone reservoirs: samples from Upper Triassic Yanchang Formation,Ordos Basin,China. Marine and Petroleum Geology,167: 107001.
[32]	Lundberg S M, Lee S I. 2017. A unified approach to interpreting model predictions. In: The 31st International Conference on Neural Information Processing Systems. New York: Curran Associates Inc.: 4768-4777.
[33]	Luo J C, Yan J P, Liao M J, Wang M, Geng B, Hu Q H. 2024. Evaluation of pore structure characteristics of deep clastic rocks in the Huangliu Formation of LD-X area,Yinggehai Basin. Marine and Petroleum Geology, 167: 106969.
[34]	Ma Y Y, Qiao Y H, Chen M X, Rui D N, Zhang X X, Liu W J, Ye L. 2024. How small is big enough? big data-driven machine learning predictions for a full-scale wastewater treatment plant. Water Research, 274: 123041. doi: 10.1016/j.watres.2024.123041 URL
[35]	Maldar R, Ranjbar-karami R, Behdad A, Bagherzadeh S. 2022. Reservoir rock typing and electrofacies characterization by integrating petrophysical properties and core data in the Bangestan reservoir of the Gachsaran oilfield,the Zagros basin,Iran. Journal of Petroleum Science and Engineering, 210: 110080.
[36]	Mao J Y, Chen J, Deng Y J. 2025. Optimization strategy for ensemble learning models based on fusing resampling,adaptive dimensionality reduction,and Optuna in intelligent flight technology evaluation. Aerospace Science and Technology, 162: 110251.
[37]	Moreno-barea F J, Jerez J M, Franco L. 2020. Improving classification accuracy using data augmentation on small data sets. Expert Systems with Applications, 161: 113696. doi: 10.1016/j.eswa.2020.113696 URL
[38]	Noble W S. 2006. What is a support vector machine? Nature Biotechnology, 24(12): 1565-1567. doi: 10.1038/nbt1206-1565 pmid: 17160063
[39]	Pittman E D. 1992. Relationship of porosity and permeability to various parameters derived from mercury injection-capillary pressure curves for Sandstone. AAPG Bulletin, 76(2): 191-198.
[40]	Ren D Z, Zhou D S, Liu D K, Dong F J, Ma S W, Huang H. 2019. Formation mechanism of the Upper Triassic Yanchang Formation tight sandstone reservoir in Ordos Basin: take Chang 6 reservoir in Jiyuan Oil Field as an example. Journal of Petroleum Science and Engineering, 178: 497-505. doi: 10.1016/j.petrol.2019.03.021 URL
[41]	Taunk K, De S, Verma S, Swetapadma A. 2019. A brief review of nearest neighbor algorithm for learning and classification. In: The 2019 International Conference on Intelligent Computing and Control Systems(ICCS). New York: IEEE,1255-1260.
[42]	Wang F Y, Hou X M. 2025. Machine learning-based prediction of physical parameters in heterogeneous carbonate reservoirs using well log data. Energy Geoscience, 6(2): 100383.
[43]	Xiao D S, Jiang S, Thul D, Lu S F, Zhang L C. 2018. Impacts of clay on pore structure,storage and percolation of tight sandstones from the Songliao Basin,China: implications for genetic classification of tight sandstone reservoirs. Fuel, 211: 390-404. doi: 10.1016/j.fuel.2017.09.084 URL
[44]	Xu X T, Zeng L B, Dong S Q, Li H M, Liu J Z, Ji C Q. 2025. The characteristics and controlling factors of high-quality reservoirs of ultra-deep tight sandstone: a case study of the Dabei Gas Field,Tarim Basin,China. Petroleum Science, 22(9): 3473-3496. doi: 10.1016/j.petsci.2025.03.033 URL
[45]	Yang S F, Bao Z D, Wang N, Qu X F, Lin Y B, Shen J J, Awan R S. 2020. Diagenetic evolution and its impact on reservoir quality of tight sandstones: a case study of the Triassic Chang 6 Member,Ordos Basin,northwest China. Marine and Petroleum Geology, 117: 104360.
[46]	Yang T, Tang H M, Dai J W, Wang H, Wen X, Wang M, He J, Wang M N. 2025. Quantitative classification and prediction of pore structure in low porosity and low permeability sandstone: a machine learning approach. Geoenergy Science and Engineering, 247: 213708.
[47]	Zhang S B, Wang S S, Huo J T, Gong C L, Li Z Q, Liu J Q, Xue R B, Yan Y H, Li B H, Shi Y H, Zhou B. 2025. Exploring aerosol vertical distributions and their influencing factors: insight from MAX-DOAS and machine learning. Environmental Science & Technology, 59(23): 11616-11627. doi: 10.1021/acs.est.4c14483 URL
[48]	Zhang Z, Zhang H, Li J, Cai Z X. 2021. Permeability and porosity prediction using logging data in a heterogeneous dolomite reservoir: an integrated approach. Journal of Natural Gas Science and Engineering, 86: 103743. doi: 10.1016/j.jngse.2020.103743 URL
[49]	Zhao W W, Zhang Z H, Liao J B, Zhang J W, Zhang W T. 2024. Prediction method for the porosity of tight sandstone constrained by lithofacies and logging resolution. Marine and Petroleum Geology, 170: 107114. doi: 10.1016/j.marpetgeo.2024.107114 URL
[50]	Zou C N, Yang Z, He D B, Wei Y S, Li J, Jia A L, Chen J J, Zhao Q, Li Y L, Li J, Yang S. 2018. Theory,technology and prospects of conventional and unconventional natural gas. Petroleum Exploration and Development, 45(4): 604-618. doi: 10.1016/S1876-3804(18)30066-1 URL

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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^*

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

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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*

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

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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^*