基于生成对抗网络的页岩三维数字岩芯构建

doi:10.11885/j.issn.1674-5086.2021.01.15.02

西南石油大学学报(自然科学版) ›› 2021, Vol. 43 ›› Issue (5): 73-83.DOI: 10.11885/j.issn.1674-5086.2021.01.15.02

基于生成对抗网络的页岩三维数字岩芯构建

杨永飞^1,2, 刘夫贵², 姚军², 宋华军³, 王民⁴

1. 深层油气重点实验室·中国石油大学(华东), 山东青岛 266580;
2. 中国石油大学(华东)石油工程学院, 山东青岛 266580;
3. 中国石油大学(华东)海洋与空间信息学院, 山东青岛 266580;
4. 中国石油大学(华东)地球科学与技术学院, 山东青岛 266580

收稿日期:2021-01-15 发布日期:2021-11-05
通讯作者: 杨永飞,E-mail:yangyongfei@upc.edu.cn
作者简介:杨永飞,1982年生,男,汉族,山东平邑人,副教授,博士,主要从事数字岩芯与纳微渗流、油气田开发方面的研究工作。E-mail:yangyongfei@upc.edu.cn
刘夫贵,1996年生,男,汉族,山东临沂人,硕士研究生,主要从事数字岩芯、深度学习、图像处理方面的研究工作。E-mail:s19020140@s.upc.edu.cn
姚军,1964年生,男,汉族,山东平邑人,教授,博士,主要从事油气渗流、数值试井解释、非常规油气藏开发、智能油田理论方面的研究工作。E-mail:yaojunhdpu@126.com
宋华军,1978年生,男,汉族,山东威海人,副教授,博士,主要从事实时目标跟踪算法及无线通信信号处理方面的研究工作。E-mail:huajun.song@upc.edu.cn
王民,1981年生,男,汉族,河北石家庄人,教授,博士,主要从事页岩油气地质与资源评价方面的研究工作。E-mail:wangm@upc.edu.cn
基金资助:
山东省自然科学基金（ZR2019JQ21）；中央高校基本科研业务费专项（20CX02113A）

Reconstruction of 3D Shale Digital Rock Based on Generative Adversarial Network

YANG Yongfei^1,2, LIU Fugui², YAO Jun², SONG Huajun³, WANG Min⁴

1. Key Laboratory of Deep Oil & Gas, China University of Petroleum(East China), Qingdao, Shandong 266580, China;
2. School of Petroleum Engineering, China University of Petroleum(East China), Qingdao, Shandong 266580, China;
3. School of Oceanography and Space Informatics, China University of Petroleum(East China), Qingdao, Shandong 266580, China;
4. School of Geosciences, China University of Petroleum(East China), Qingdao, Shandong 266580, China

Received:2021-01-15 Published:2021-11-05

摘要/Abstract

摘要： 页岩油气藏孔隙结构复杂，岩芯获取困难，准确表征页岩储层孔隙结构是研究页岩储层内流体渗流规律的关键。基于真实页岩岩芯的三维聚焦离子束扫描图像，对原始生成对抗网络模型的结构重新设计，同时，为了保证重建结果可以充分反映页岩岩芯的孔隙结构信息，增大了训练样本的尺寸，以此训练生成模型，进而生成页岩三维数字岩芯，对比分析了重建数字岩芯和原始岩芯的孔隙度，并基于重建数字岩芯提取了孔隙网络模型，分析了页岩孔隙结构性质。结果显示，重建岩芯的孔隙度、孔隙空间结构、连通性以及孔隙喉道的配位关系与原始岩芯具有很高的一致性，由此验证了生成模型可以实现三维页岩数字岩芯的构建。最后，构建了多个页岩数字岩芯，计算了多个孔隙结构参数的均值及变化区间，证明了生成的数字岩芯具有稳定的孔隙空间特征，训练好的生成模型具有良好的稳定性。

关键词: 页岩, 数字岩芯, 生成对抗网络, 图像重建, 参数评价

Abstract: The pore structure of shale oil reservoir is complex, and the shale cores are hard to acquire. Accurately characterizing the pore structure of shale reservoir is the key to the study on the fluid seepage law in shale reservoir. Based on the three-dimensional focused ion beam scanning (3D FIB SEM) images of real shale cores, the structure of the original generative adversarial network model is redesigned. At the same time, to ensure that the reconstruction results can fully reflect the pore structure information of the shale core, the size of the training sample is increased, and the model is trained to generate three-dimensional shale digital rock. The porosity of the reconstructed digital rock and the original core are compared, and the pore network model is extracted from the reconstructed digital rock, then the pore structure properties are analyzed. The porosity, pore and throat sizes, connectivity, and coordination relationship of the reconstructed digital rock are highly in agreement with the original cores, which verifies that the generative model can generate high-quality three-dimensional shale digital rock. Finally, several digital rocks are generated, and the mean value and variation range of various pore structure parameters are calculated. It is proved that the generated digital rocks have stable pore space characteristics, and the trained generative model has good stability.

Key words: shale, digital rock, generative adversarial networks, image reconstruction, parameter evaluation

中图分类号:

TE319

杨永飞, 刘夫贵, 姚军, 宋华军, 王民. 基于生成对抗网络的页岩三维数字岩芯构建[J]. 西南石油大学学报(自然科学版), 2021, 43(5): 73-83.

YANG Yongfei, LIU Fugui, YAO Jun, SONG Huajun, WANG Min. Reconstruction of 3D Shale Digital Rock Based on Generative Adversarial Network[J]. Journal of Southwest Petroleum University(Science & Technology Edition), 2021, 43(5): 73-83.

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基于生成对抗网络的页岩三维数字岩芯构建

Reconstruction of 3D Shale Digital Rock Based on Generative Adversarial Network

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