数字岩心渗流物理特性智能预测研究进展

梁建勋; 贾宁洪; 吕伟峰; 张青; 谢驰宇

doi:10.21656/1000-0887.460219

数字岩心渗流物理特性智能预测研究进展

doi: 10.21656/1000-0887.460219

梁建勋^1,2,3,
贾宁洪^2,3,4, ,,
吕伟峰^2,3,4,
张青^3,4,
谢驰宇⁵

1. 中国科学院大学，北京 100049;
2. 中国科学院渗流流体力学研究所，河北廊坊 065007;
3. 提高油气采收率全国重点实验室，北京 100083;
4. 中国石油勘探开发研究院，北京 100083;
5. 北京航空航天大学宇航学院，北京 100191

基金项目:

中国石油天然气股份有限公司科技项目“油气勘探开发人工智能关键技术研究”（2023DJ84）

详细信息

作者简介:
梁建勋(2001—),男,硕士生(E-mail: liangjianxun24@mails.ucas.ac.cn)；贾宁洪(1981—),男,高级工程师(通信作者. E-mail: jianinghong@petrochina.com.cn)；谢驰宇(1990—),男,教授,博士生导师(通信作者. E-mail: xiechiyu@buaa.edu.cn).

通讯作者:
贾宁洪(1981—),男,高级工程师(通信作者. Email: jianinghong@petrochina.com.cn)；

中图分类号: O357.3
计量
- 文章访问数: 11
- HTML全文浏览量: 2
- PDF下载量: 5
- 被引次数: 0
出版历程
- 收稿日期: 2025-12-02
- 修回日期: 2025-12-24
- 网络出版日期: 2026-07-23

Advances in Intelligent Prediction of the Percolation Properties of Digital Rocks

Liang Jianxun^1,2,3,
Jia Ninghong^{2,3,4
, ,},
Lü Weifeng^2,3,4,
Zhang Qing^3,4,
Xie Chiyu⁵

1. University of Chinese Academy of Sciences, Beijing 100049, P.R. China;
2. Institute of Porous Flow & Fluid Mechanics, Chinese Academy of Sciences, Langfang, Hebei 065007, P.R. China;
3. State Key Laboratory of Enhanced Oil and Gas Recovery,Beijing 100083, P.R. China;
4. Research Institute of Petroleum Exploration and Development, PetroChina, Beijing 100083, P.R. China;
5. School of Astronautics, Beihang University, Beijing 100191, P.R. China

摘要

摘要: 岩心分析是储层评价的基石，数字岩心技术（digital rock physics, DRP）通过三维成像与数值模拟，实现了对储层微观结构与渗流特性的定量表征.本文总结了数字岩心技术从“所见为所得”结构表征范式，到“所算为所得”数值计算范式，再到“所学为所得”智能预测范式的演进历程.前两代范式面临代表性单元体（representative elementary volume, REV）尺度难寻、数值计算成本高昂等核心瓶颈.本文从结构特性、单相渗流特性（渗透率）以及多相渗流特性（相对渗透率、毛管压力、润湿性）等方面着重梳理了“所学为所得”的数字岩心分析技术进展.该范式以深度学习为核心，为突破传统瓶颈提供了革命性路径：一方面，利用生成对抗网络（generative adversarial networks, GANs）与超分辨率技术（superresolution, SR），通过数据驱动重建和增强数字岩心，缓解了REV难题；另一方面，通过构建卷积神经网络（convolutional neural network, CNN）等高效代理模型，将物性预测速度提升数个数量级，有效解决了计算耗时问题.最后，本文探讨了当前数字岩心智能预测技术在数据依赖、模型泛化与物理可解释性方面面临的挑战，并展望了将物理机理深度融入AI模型、推动多尺度数据融合以及构建储层“数字孪生”等未来发展方向，支撑我国智慧油田建设.
- 数字岩心 /
- 人工智能 /
- 深度学习 /
- 渗流物性 /
- 参数预测
Abstract: Core analysis is the cornerstone of reservoir evaluation. The digital rock physics (DRP) enables the quantitative characterization of reservoir microstructures and flow properties through 3D imaging and numerical simulation. The evolution of DRP paradigms was reviewed from the 1stgeneration “What you see is what you get” (geometric characterization) and the 2ndgeneration “What you calculate is what you get” (numerical simulation), to the emerging 3rdgeneration “What you learn is what you get” (intelligent prediction). The 1st 2 paradigms face 2 core bottlenecks: the difficulty in determining the representative elementary volume (REV) and the prohibitive computational costs of numerical simulations. The progress of the 3rdgeneration intelligent prediction paradigm was systematically summarized, with the focus on structural properties, singlephase permeability, and multiphase flow properties (relative permeability, capillary pressure, and wettability). Centered on deep learning, this paradigm offers revolutionary pathways to overcome traditional limitations: on one hand, the generative adversarial networks (GANs) and the superresolution (SR) techniques were employed for datadriven reconstruction to mitigate REV challenges; on the other hand, the efficient surrogate models, such as the convolutional neural networks (CNNs), were constructed to accelerate property prediction by orders of magnitude. Finally, the current challenges regarding data dependence, model generalization, and physical interpretability were discussed to outlines future directions, including integrating physical mechanisms into AI models (physicsinformed AI), promoting multiscale data fusion, and constructing reservoir “digital twins” to support the development of smart oilfields.
- digital core /
- artificial intelligence /
- deep learning /
- percolation properties /
- parameter prediction

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数字岩心渗流物理特性智能预测研究进展

doi: 10.21656/1000-0887.460219

作者简介:
梁建勋(2001—),男,硕士生(E-mail: liangjianxun24@mails.ucas.ac.cn)；贾宁洪(1981—),男,高级工程师(通信作者. E-mail: jianinghong@petrochina.com.cn)；谢驰宇(1990—),男,教授,博士生导师(通信作者. E-mail: xiechiyu@buaa.edu.cn).

通讯作者:
贾宁洪(1981—),男,高级工程师(通信作者. Email: jianinghong@petrochina.com.cn)；

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Advances in Intelligent Prediction of the Percolation Properties of Digital Rocks

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留言板

数字岩心渗流物理特性智能预测研究进展

doi: 10.21656/1000-0887.460219

作者简介: 梁建勋(2001—),男,硕士生(E-mail: liangjianxun24@mails.ucas.ac.cn)；贾宁洪(1981—),男,高级工程师(通信作者. E-mail: jianinghong@petrochina.com.cn)；谢驰宇(1990—),男,教授,博士生导师(通信作者. E-mail: xiechiyu@buaa.edu.cn).

通讯作者: 贾宁洪(1981—),男,高级工程师(通信作者. Email: jianinghong@petrochina.com.cn)；

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出版历程

Advances in Intelligent Prediction of the Percolation Properties of Digital Rocks

计量

出版历程

目录

作者简介:
梁建勋(2001—),男,硕士生(E-mail: liangjianxun24@mails.ucas.ac.cn)；贾宁洪(1981—),男,高级工程师(通信作者. E-mail: jianinghong@petrochina.com.cn)；谢驰宇(1990—),男,教授,博士生导师(通信作者. E-mail: xiechiyu@buaa.edu.cn).

通讯作者:
贾宁洪(1981—),男,高级工程师(通信作者. Email: jianinghong@petrochina.com.cn)；