陆相页岩储层薄片超分辨率增强方法

doi:10.11743/ogg20210517

Abstract

Abstract:

Despite a great progress made in the exploration and development of shale gas and oil in recent years, the study on the pores, organic matter and mineral composition of shale reservoirs at a microscopic scale is still a challenge with thin section analysis for conventional reservoirs. In order to solve this problem, this study introduces a super-resolution technology to improve thin section image quality for revealing micro-characteristics of shale reservoirs. A set of super-resolution models are established based on generative adversarial networks and corresponding content loss functions are also set up for thin-section images. Application of the technology to the processing of actual data from lacustrine shale gas reservoirs in the Yanchang Formation in Ordos Basin has yielded positive results, demonstrating quantitatively and qualitatively its applicability, accuracy and reliability for unconventional reservoir assessment.

Key words: microscopic physical property, super-resolution, data enhancement, thin section, shale reservoir, Ordos Basin

CLC Number:

TE135

Chao Guo, Qianping Zhao, Gang Liu, Shiyan Hao, Chao Gao, Jianbo Sun, Chao Liu, Yiyi Chen. Super-resolution imaging of thin sections for lacustrine shale reservoirs[J]. Oil & Gas Geology, 2021, 42(5): 1202-1209.

Figures/Tables 10

Fig.1

Fig.2

Fig.3

Fig.4

Table 1

Fig.5

Fig.6

Fig.7

Table 2

Fig.8

References 19

1	王卫星, 谭嘉玉. 基于分数阶微分和形态学多级合成的岩石节理裂隙图像分割[J]. 计算机应用, 2010, 30 (4): 929- 931.
	Wang Weixing , Tan Jiayu . Rock joint image segmentation based on fractional differential and multi-grade combination in mathematical morphology[J]. Journal of Computer Applications, 2010, 30 (4): 929- 931.
2	Yang X , Zhang G , Lu J , et al. A kernel fuzzy c-means clustering-based fuzzy support vector machine algorithm for classification problems with outliers or noises[J]. IEEE Transactions on Fuzzy Systems, 2011, 19 (1): 105- 115. doi: 10.1109/TFUZZ.2010.2087382
3	Zhang Y , Fu L Y , Zhang L , et al. Finite difference modeling of ultrasonic propagation (coda waves) in digital porous cores with un-split convolutional PML and rotated staggered grid[J]. Journal of Applied Geophysics, 2014, 104 (5): 75- 89.
4	陈本廷, 周骛, 蔡小舒, 等. 基于K-均值聚类的岩芯偏振显微图像粒径分析[J]. 上海理工大学学报, 2016, 38 (4): 341- 345, 351.
	Chen Benting , Zhou Wu , Cai Xiaoshu , et al. New method for the segmentation of polarizing microscope image of rock core based on K-means cluster algorithm[J]. University of Shanghai for Science and Technology, 2016, 38 (4): 341- 345, 351.
5	叶润青, 牛瑞卿, 张良培. 基于多尺度分割的岩石图像矿物特征提取及分析[J]. 吉林大学学报(地球科学版), 2011, 41 (4): 1253- 1261.
	Ye Runqing , Niu Ruiqing , Zhang Liangpei . Mineral features extraction and analysis based om multiresolution segmentation of petrographic images[J]. Journal of Jilin University(Earth Science Edition), 2011, 41 (4): 1253- 1261.
6	姚军, 赵秀才, 衣艳静, 等. 储层岩石微观结构性质的分析方法[J]. 中国石油大学学报(自然科学版), 2007, 31 (1): 80- 86.
	Yao Jun , Zhao Xiucai , Yi Yanjing , et al. Analysis methods for reservoir rock's microstructure[J]. Journal of China University of Petroleum, 2007, 31 (1): 80- 86.
7	Li Z , Teng Q , He X , et al. Sparse representation-based volumetric super-resolution algorithm for 3D CT images of reservoir rocks[J]. Journal of Applied Geophysics, 2017, 144, 69- 77. doi: 10.1016/j.jappgeo.2017.04.013
8	Wang Y D, Armstrong, Mostaghimi P. Super resolution convolutional Neural Network Models for enhancing resolution of rock Micro-CT Images[EB/OL]. 2019. arXiv: 1904.07470[cs. CV]. https://arxiv.org/abs/1904.07470.
9	Ye Liu , C Guo , J Cao , et al. A new resolution enhancement method for sandstone thin-section images using perceptual GAN[J]. Journal of Petroleum Science and Engineering, 2020, 195 (2): 107921.
10	Chao Dong , Chen Change Loy , Kaiming He , et al. Image super-resolution using deep convolutional networks[J]. IEEE Trans Pattern Anal Mach Intell, 2016, 38 (2): 295- 307. doi: 10.1109/TPAMI.2015.2439281
11	Chao D, Chen C L, Tang X, et al. Accelerating the super-resolution convolutional neural network[C]//European Conference on Computer Vision, Springer, Amsterdam, 2016.
12	Jiwon Kim, Jung Kwon Lee, Kyoung Mu Lee. Accurate image super-resolution using very deep convolutional networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE, Las Vegas, 2016, 1646-1654.
13	Yu J, Fan Y, Yang J, et al. Wide activation for efficient and accurate image super-resolution[EB/OL]. 2018 arXiv: 1808.08718v1[cs. CV]. https://arxiv.org/abs/1808.08718v1.
14	Christian Ledig, Theis L, Husz'ar F, et al. Photo-realistic single image super resolution using a generative adversarial network[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2017.
15	Guo Y, Chen J, Wang J, et al. Closed-loop Matters: Dual regression networks for single image super-resolution[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2020.
16	Zhou Yuemei, Wu Gaochang, Fu Ying, et al. Cross-MPI: Cross-scale stereo for image super-resolution using multiplane images[EB/OL]. 2011. arXiv: 2011.14631[cs. CV]. https://arxiv.org/abs/2011.14631
17	Jolicoeur Martineau A. The relativistic discriminator: a key element missing from standard GAN[EB/OL]. 2018 arXiv: 1807.00734[cs. LG]. https://arxiv.org/abs/1807.00734.
18	Zhang L , Zhang L , Mou X , et al. FSIM: A feature similarity index for image quality assessment[J]. IEEE Transactions on Image Processing, 2011, 20 (8): 2378- 2386.
19	Xue W , Zhang L , Mou X , et al. Gradient magnitude similarity deviation: A highly efficient perceptual image quality index[J]. IEEE Transactions on Image Processing, 2014, 23 (2): 684- 695.

Batch size	训练时间/min	FSIM均值	FSIM方差	GMSD均值	GMSD方差
5	496	0.723 8	0.002 234	0.194 2	0.001 365
10	429	0.725 7	0.002 296	0.192 8	0.001 408
20	436	0.726 3	0.002 377	0.192 9	0.001 396
30	436	0.723 8	0.002 261	0.194 6	0.001 353

模拟	数值	PNSR	SSIM	IMMSE	FSIM	GMSD	NIQE
Bicubic	期望值	19.912 1	0.879 040	694.784 100	0.880 87	0.240 49	6.926 00
Bicubic	方差	1.384 1	0.028 143	197.633 300	0.174 93	0.172 10	0.261 74
SR	期望值	31.905 0	0.982 990	55.632 820	0.999 10	0.044 996	5.434 90
SR	方差	2.875 9	0.011 865	5.632 482	0.006 12	0.021 924	0.666 08

[1]	Chiyang Liu, Jianqiang Wang, Dongdong Zhang, Hongge Zhao, Junfeng Zhao, Lei Huang, Wenqing Wang, Yang Qin. Genesis of rich hydrocarbon resources and their occurrence and accumulation characteristics in the Ordos Basin [J]. Oil & Gas Geology, 2021, 42(5): 1011-1029.
[2]	Zhanli Ren, Kai Qi, Jinbu Li, Xiaoju Huo, Junping Cui, Peng Yang, Kun Wang, Zhanjun Chen, Guilin Yang. Thermodynamic evolution and hydrocarbon accumulation in the Ordos Basin [J]. Oil & Gas Geology, 2021, 42(5): 1030-1042.
[3]	Xiangbo Li, Hongbo Wang, Junping Huang, Caili Zhang, Yan Zhang, Yating Wang, Lei Zhang, Jing Wang, Huaqing Liu. Characteristics of unconformity resulted from Huaiyuan Movement in Ordos Basin and its significance for oil and gas exploration [J]. Oil & Gas Geology, 2021, 42(5): 1043-1055.
[4]	Faqi He, Rong Qi, Fubin Wang, Jie Deng, Li Cheng, Tianle Hu. Tectonic genesis of Triassic Yanchang Formation valley systems, southern Ordos Basin [J]. Oil & Gas Geology, 2021, 42(5): 1056-1062.
[5]	Xinping Zhou, Qing He, Jiangyan Liu, Shixiang Li, Tian Yang. Features and origin of deep-water debris flow deposits in the Triassic Chang 7 Member, Ordos Basin [J]. Oil & Gas Geology, 2021, 42(5): 1063-1077.
[6]	Bojiang Fan, Yue Jin, Liang Shi, Yating Li, Weichang Chen. Shale oil exploration potential in central Ordos Basin: A case study of Chang 7 lacustrine shale [J]. Oil & Gas Geology, 2021, 42(5): 1078-1088.
[7]	Yaxiong Zhang. Source rock characterization: The dark mudstone in Chang 7 Member of Triassic, central Ordos Basin [J]. Oil & Gas Geology, 2021, 42(5): 1089-1097.
[8]	Zhaobing Chen, Zhenyu Zhao, Ling Fu, Jianrong Gao, Wei Song, Xinjing Chen. Interstitial matter and its impact on reservoir development in Chang 6 deepwater tight sandstone in Huaqing area, Ordos Basin [J]. Oil & Gas Geology, 2021, 42(5): 1098-1111.
[9]	Baohong Shi, Xinyu Qin, Caili Zhang, Wen Liu, Gang Liu, Chanyuan Shi, Lei Zhang, Zishu Yong. Insights on factors causing differential enrichment of Chang 6 Member in Jiyuan area, Ordos Basin [J]. Oil & Gas Geology, 2021, 42(5): 1112-1123.
[10]	Jinbu Li, Jiping Wang, Long Wang, Bin Fu, Hui Xia, Zhixiao Li. Paleogeomorphologic restoration and its controlling effect on deposition of delta-front sand bodies: A case study of Shan 1³ sub-member of the Permian Shanxi Formation, Qingyang gas field, Ordos Basin [J]. Oil & Gas Geology, 2021, 42(5): 1136-1145, 1158.
[11]	Chang Liu, Daomin Zhang, Chao Li, Yuanyuan Lu, Shanshan Yu, Mingqiang Guo. Upper Paleozoic tight gas sandstone reservoirs and main controls, Linxing block, Ordos Basin [J]. Oil & Gas Geology, 2021, 42(5): 1146-1158.
[12]	Juntao Zhang, Xiaohui Jin, Ning Gu, Changrong Bian, Jiaqi Yang, Zhiliang He. Differences and development patterns of karst reservoirs in Majiagou Formation, northern Ordos Basin [J]. Oil & Gas Geology, 2021, 42(5): 1159-1168, 1242.
[13]	Dongdong Zhang, Wenhui Liu, Xiaofeng Wang, Houyong Luo, Qingtao Wang, Yining Li, Fengjiao Li. Genetic types and characteristics of deep oil and gas plays [J]. Oil & Gas Geology, 2021, 42(5): 1169-1180.
[14]	Fangzheng Jiao. FSV estimation and its application to development of shale oil via volume fracturing in the Ordos Basin [J]. Oil & Gas Geology, 2021, 42(5): 1181-1188.
[15]	Pang Yang, Zhanli Ren, Zhao Jianxin, Duc Nguyen Ai, Feng Yuexing, Kai Qi, Kun Wang. Tectonic evolution analysis constrained jointly by in-situ calcite U-Pb dating and apatite fission track for southwestern Ordos Basin [J]. Oil & Gas Geology, 2021, 42(5): 1189-1201.

Super-resolution imaging of thin sections for lacustrine shale reservoirs

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 10

References 19

Related Articles 15

Recommended Articles

Metrics