碎屑岩储层智能表征与建模方法研究现状及展望<sup>*</sup>

doi:10.7605/gdlxb.2025.090

古地理学报 ›› 2025, Vol. 27 ›› Issue (4): 903-923. doi: 10.7605/gdlxb.2025.090

碎屑岩储层智能表征与建模方法研究现状及展望^*

岳大力¹^,²^,³(), 李伟¹^,²(), 王武荣¹^,², 孙盼科¹^,², 吴胜和¹^,²^,³, 徐振华¹^,², 刘磊¹^,²^,³, 邬德刚¹^,²^,³, 屈林博¹^,², 任柯宇¹^,², 林津¹^,², 张姝琪¹^,²

1 油气资源与工程全国重点实验室,中国石油大学(北京),北京 102249
2 中国石油大学(北京)地球科学学院,北京 102249
3 中国石油大学(北京)人工智能学院,北京 102249

收稿日期:2025-06-11 修回日期:2025-06-18 出版日期:2025-08-01 发布日期:2025-07-30
通讯作者: 李伟,男,1990年生,副教授,从事油气田开发地质与储层智能表征方面的教学科研工作。E-mail: wei_li@cup.edu.cn。
作者简介:
岳大力,男,1974年生,教授,博士生导师,从事油气田开发地质方面的教学科研工作。E-mail: yuedali@cup.edu.cn。
基金资助:
*国家自然科学基金项目(42272186); 国家自然科学基金项目(42202109); 国家自然科学基金项目(42302128); 国家自然科学基金项目(42412179)

Advances and perspectives in intelligent characterization and modeling of clastic reservoirs

YUE Dali¹^,²^,³(), LI Wei¹^,²(), WANG Wurong¹^,², SUN Panke¹^,², WU Shenghe¹^,²^,³, XU Zhenhua¹^,², LIU Lei¹^,²^,³, WU Degang¹^,²^,³, QU Linbo¹^,², REN Keyu¹^,², LIN Jin¹^,², ZHANG Shuqi¹^,²

1 State Key Laboratory of Petroleum Resources and Engineering,China University of Petroleum(Beijing),Beijing 102249,China
2 College of Geosciences,China University of Petroleum(Beijing),Beijing 102249,China
3 College of Artificial Intelligence, China University of Petroleum(Beijing), Beijing 102249, China

Received:2025-06-11 Revised:2025-06-18 Online:2025-08-01 Published:2025-07-30
Contact: LI Wei,born in 1990,associate professor,is engaged in teaching and scientific research on oil & gas field development geology and intelligent reservoir characterization. E⁃mail: wei_li@cup.edu.cn.
About author:
YUE Dali,born in 1974,professor and Ph.D. supervisor,is engaged in teaching and scientific research on oil & gas field development geology. E-mail: yuedali@cup.edu.cn.
Supported by:
National Natural Science Foundation of China(42272186); National Natural Science Foundation of China(42202109); National Natural Science Foundation of China(42302128); National Natural Science Foundation of China(42412179)

摘要/Abstract

摘要：

碎屑岩储层是中国乃至全球油气资源的重要载体。受限于碎屑岩储层非均质强、地下表征资料相对不足的客观条件,传统的表征与建模技术长期以来难以满足地下储层精细勘探与高效开发的需求。21世纪以来,众多学者逐步尝试将人工智能技术引入碎屑岩储层表征与建模领域,并在近10年取得快速发展。鉴于此,作者系统梳理了智能化技术在碎屑岩储层表征与建模领域的发展历程与研究现状,重点阐述了储层参数测井智能解释、智能化断层与地层构造解析、井震融合智能储层预测、碎屑岩储层智能三维地质建模的最新研究进展与应用效果,并分析了不同智能化储层表征与建模技术面临的挑战与未来的发展方向。总体而言,上述碎屑岩储层智能表征技术在不同程度上面临着高质量样本不足、智能学习模型泛化能力较差、知识驱动与数据驱动融合程度低等难题,未来仍有巨大的发展空间与良好的应用前景。

关键词: 碎屑岩, 储层表征, 三维地质建模, 测井解释, 井震融合, 人工智能

Abstract:

Clastic rock reservoirs serve as critical carriers of hydrocarbon resources both in China and around the world. However,due to inherent limitations such as strong heterogeneity and insufficient subsurface characterization data,traditional methods of reservoir characterization and modeling have struggled to fulfill the demands for high-resolution exploration and efficient development. Since the 21th century,researchers have progressively integrated artificial intelligence(AI)techniques into the field of clastic reservoir characterization and modeling,resulting in significant advancements over the past decade. These innovations have significantly improved both the accuracy and efficiency of reservoir characterization. In this context,this paper systematically reviews the development history and current research status of intelligent technologies in clastic reservoir characterization and modeling. It highlights recent progress and application outcomes in areas such as intelligent well log interpretation for reservoir parameters,AI-based fault and stratigraphic framework analysis,intelligent reservoir prediction through well-seismic integration,and intelligent 3D geological modeling. Furthermore,we discuss the challenges faced by various intelligent approaches and outlines future directions for their development. Overall,these intelligent characterization techniques have made significant advances and demonstrated positive outcomes in practical applications. Nevertheless,they also face multiple challenges,including a lack of high-quality training samples,suboptimal generalization capabilities of learning models,and inadequate coupling of knowledge-driven with data-driven approaches. Despite these limitations,there remains significant potential for advancement,with promising application prospects emerging across reservoir characterization workflows.

Key words: clastic rock, reservoir characterization, 3D geological modeling, well-log interpretation, well-seismic integration, artificial intelligence

中图分类号:

P631

岳大力, 李伟, 王武荣, 孙盼科, 吴胜和, 徐振华, 刘磊, 邬德刚, 屈林博, 任柯宇, 林津, 张姝琪. 碎屑岩储层智能表征与建模方法研究现状及展望^*[J]. 古地理学报, 2025, 27(4): 903-923.

YUE Dali, LI Wei, WANG Wurong, SUN Panke, WU Shenghe, XU Zhenhua, LIU Lei, WU Degang, QU Linbo, REN Keyu, LIN Jin, ZHANG Shuqi. Advances and perspectives in intelligent characterization and modeling of clastic reservoirs[J]. Journal of Palaeogeography（Chinese Edition）, 2025, 27(4): 903-923.

http://www.gdlxb.cn/CN/Y2025/V27/I4/903

图/表 13

图 1 储层参数测井智能解释流程(据Wang et al., 2025;有修改)

Fig.1 Intelligent logging interpretation workflow for reservoir parameters(modified from Wang et al., 2025)

图 2 某取心井的测井响应及物性智能解释(据邬德刚等,2025;有修改)

Fig.2 Logging response and intelligent interpretation of physical property of a coring well(modified from Wu et al.,2025)

图 3 测井曲线特征优化与均衡采样 a—基于离散小波的GR测井曲线重构,其中K为离散小波分解次数; b—基于SMOTE算法的合成过采样

Fig.3 Optimization of logging curve characteristics and balanced sampling

表 1 储层参数测井智能解释方法的优劣势以及适用性

Table 1 Advantages,disadvantages,and applicability of intelligence methods for reservoir petrophysical log interpretation

算法类型	算法名称	优点	缺点	适用条件
传统机器学习	支持向量机 (SVM)	能处理高维数据,小样本表现好,可解决非线性问题	参数选择敏感、大规模数据训练慢、性能低	数据量小、特征维度不高的测井数据解释
	随机森林 (Random forest)	泛化能力强,不易过拟合,处理高维和缺失值	模型复杂度高、计算资源消耗大	复杂储层、多参数非线性关系明显的解释任务
	梯度提升树 (如XGBoost)	预测精度高,能自动处理缺失值,特征选择	参数调优复杂、训练时间较长	高精度要求的储层参数预测、特征重要性分析
	K近邻 (KNN)	算法简单易实现,对异常值鲁棒性强	对维度灾难和不平衡数据敏感	数据量适中、局部特征重要性高
深度学习	前馈神经网络(FNN)	学习复杂非线性关系,强大特征学习能力	需要大量数据、易过拟合、黑箱特性	大规模测井数据,复杂物性参数预测,岩性/流体识别
	卷积神经网络(CNN)	局部特征提取能力强,适合提取测井曲线特征	模型复杂,计算量大,需要大量标记数据	测井曲线图像识别、局部特征明显的解释任务
	循环神经网络 (如RNN/LSTM)	适合序列数据,捕捉序列依赖	需要规整输入、对序列长度敏感	测井时间序列分析、考虑纵向变化的解释
	Transformer	适合长距离依赖建模,多模态融合	数据需求大,计算资源消耗较高	高维测井曲线序列建模,非均质储层参数预测
混合集成模型	Stacking集成学习	模型性能提升,灵活性强	计算复杂度高和参数调整难度大	适应处理测井与储层参数的复杂非线性关系
	PSO-XGBoost	全局搜索能力强,泛化性好	计算成本较高,可能陷入过拟合	数据量适中的复杂非均质储层参数解释
	CNN-LSTM等	结合空间和时序特征提取优势	模型设计调试复杂,需深入理解多算法	测井数据具有多种特征类型,结合多层面信息,解决复杂解释难题

表 1 储层参数测井智能解释方法的优劣势以及适用性

Table 1 Advantages,disadvantages,and applicability of intelligence methods for reservoir petrophysical log interpretation

算法类型	算法名称	优点	缺点	适用条件
传统机器学习	支持向量机 (SVM)	能处理高维数据,小样本表现好,可解决非线性问题	参数选择敏感、大规模数据训练慢、性能低	数据量小、特征维度不高的测井数据解释
	随机森林 (Random forest)	泛化能力强,不易过拟合,处理高维和缺失值	模型复杂度高、计算资源消耗大	复杂储层、多参数非线性关系明显的解释任务
	梯度提升树 (如XGBoost)	预测精度高,能自动处理缺失值,特征选择	参数调优复杂、训练时间较长	高精度要求的储层参数预测、特征重要性分析
	K近邻 (KNN)	算法简单易实现,对异常值鲁棒性强	对维度灾难和不平衡数据敏感	数据量适中、局部特征重要性高
深度学习	前馈神经网络(FNN)	学习复杂非线性关系,强大特征学习能力	需要大量数据、易过拟合、黑箱特性	大规模测井数据,复杂物性参数预测,岩性/流体识别
	卷积神经网络(CNN)	局部特征提取能力强,适合提取测井曲线特征	模型复杂,计算量大,需要大量标记数据	测井曲线图像识别、局部特征明显的解释任务
	循环神经网络 (如RNN/LSTM)	适合序列数据,捕捉序列依赖	需要规整输入、对序列长度敏感	测井时间序列分析、考虑纵向变化的解释
	Transformer	适合长距离依赖建模,多模态融合	数据需求大,计算资源消耗较高	高维测井曲线序列建模,非均质储层参数预测
混合集成模型	Stacking集成学习	模型性能提升,灵活性强	计算复杂度高和参数调整难度大	适应处理测井与储层参数的复杂非线性关系
	PSO-XGBoost	全局搜索能力强,泛化性好	计算成本较高,可能陷入过拟合	数据量适中的复杂非均质储层参数解释
	CNN-LSTM等	结合空间和时序特征提取优势	模型设计调试复杂,需深入理解多算法	测井数据具有多种特征类型,结合多层面信息,解决复杂解释难题

图 4 不同地震解释方法的断层识别效果(据Lou et al., 2021) a—基于相干体的断层表征结果; b—基于卷积神经网络的断层表征结果

Fig.4 Fault identification results by different seismic interpretation methods(after Lou et al., 2021)

图 5 知识与数据驱动的地层对比(据邬德刚等,2024a) a—地层对比模式约束; b—测井曲线相似性对比方法; c—智能地层对比结果

Fig.5 Stratigraphic correlation driven by knowledge and data(after Wu et al., 2024a)

图 6 基于Stacking 集成学习的智能储层预测(据刘磊等,2024;有修改) a—Stacking 集成学习流程; b—原始数据体最大振幅属性; c—Stacking 的分频属性智能融合结果

Fig.6 Intelligent reservoir prediction based on stacking ensemble learning(modified from Liu et al., 2024)

图 7 降低围岩干扰前后的地震属性对比(据Li et al., 2020;有修改) a—基于概念模型的三维地震正演数据体; b—上部相邻地层的实际砂体厚度; c—目的层的实际砂体厚度; d—下部相邻地层的实际砂体厚度; e—受围岩干扰的目的层RMS振幅属性; f—降低围岩干扰后的智能融合属性

Fig.7 Comparison of seismic attributes before and after reducing wall-rock interference(modified from Li et al., 2020)

图 8 埕岛油田某小层废弃河道与点坝识别流程(据Li et al., 2019a) a—分频智能融合属性图; b—上、中、下部反演切片RGB融合图; c—预测砂体厚度; d-f—连井剖面与分频遗传反演剖面

Fig.8 Workflow of interpretation of abandoned channel and point bar in a sedimentary interval,Chengdao Oilfield(after Li et al., 2019a)

图 9 埕岛油田某小层上、中、下部反演切片RGB融合图(a)与构型单元分布图(b)(据李伟等,2021)

Fig.9 RGB-blending map of the lower,middle,and upper inversion slices(a)and distribution of fluvial architecture elements(b) in a sedimentary interval,Chengdao Oilfield(after Li et al., 2021)

图 10 基于平面优势相约束的三维构型模型(据邬德刚等,2024b)

Fig.10 3D architecture model based on planar dominant facies-constrained method(after Wu et al., 2024b)

图 11 基于生成对抗网络的三维沉积相建模方法

Fig.11 3D sedimentary facies modeling method based on GANs

图 12 基于生成对抗网络的河流相随机建模结果

Fig.12 Stochastic modeling results of fluvial reservoir based on GANs

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