Characterizing multi-scale shale pore structure based on multi-experimental imaging and machine learning
An accurate and comprehensive understanding of shale pore structure is fundamental and critical for accurate reserves evaluation and efficient hydrocarbon development. Thus, by taking the shale of Paleogene Eocene Shahejie Formation in the Jiyang Depression, Bohai Bay Basin, as an example, the 2D an...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2023-08-01
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| Series: | Natural Gas Industry B |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352854023000475 |
| _version_ | 1797280219518730240 |
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| author | Jun Yao Lei Liu Yongfei Yang Hai Sun Lei Zhang |
| author_facet | Jun Yao Lei Liu Yongfei Yang Hai Sun Lei Zhang |
| author_sort | Jun Yao |
| collection | DOAJ |
| description | An accurate and comprehensive understanding of shale pore structure is fundamental and critical for accurate reserves evaluation and efficient hydrocarbon development. Thus, by taking the shale of Paleogene Eocene Shahejie Formation in the Jiyang Depression, Bohai Bay Basin, as an example, the 2D and 3D multi-resolution images of the shale microstructure are obtained by multiple imaging technologies, including X-ray computed tomography, large-field scanning electron microscopy, scanning electron microscopy and focused ion beam scanning electron microscopy. By integrating image processing and machine learning algorithms, the shale pore structure is characterized at a single scale and multi scales. The results are obtained as follows. First, the shale pore space in the study area is mainly composed of microfractures, inorganic pores, organic matters and organic pores, and exclusively shows multi-scale characteristics. Second, there are various types of inorganic pores, and abundant dissolution pores; organic matters are distributed as strips and patches, and no organic pores are found in some organic matters. Third, pores with radius less than 20 nm account for 25%, those with radius between 20 and 50 nm account for 19%, those with radius between 50 and 100 nm account for 29%, those with radius between 100 and 500 nm account for 14%, those with radius between 500 nm and 20 μm account for 11%, and those with radius between 20 and 50 μm account for 2%. Fourth, the organic pores are less connected than the inorganic pores. The connectivity between organic pores and inorganic pores plays a crucial role in hydrocarbon migration, and microfractures control fluid flow channels. Fifth, pores with radius less than 50 nm are dominantly organic pores, those with radius between 50 and 500 nm are mainly organic and inorganic pores, and microfractures mainly contribute to the pores with radius more than 500 nm. It is concluded that a single imaging experiment cannot accurately and comprehensively reveal the multi-scale micro pore structure of a shale reservoir. Through integration of multiple imaging technologies and machine learning algorithms, the shale pore structure can be recognized and characterized at both single scale and multi scales. The proposed new method provides accurate and comprehensive information of multi-scale pore structures. |
| first_indexed | 2024-03-07T16:36:59Z |
| format | Article |
| id | doaj.art-01334be55b67444c92f576183598b75a |
| institution | Directory Open Access Journal |
| issn | 2352-8540 |
| language | English |
| last_indexed | 2024-03-07T16:36:59Z |
| publishDate | 2023-08-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Natural Gas Industry B |
| spelling | doaj.art-01334be55b67444c92f576183598b75a2024-03-03T09:42:05ZengKeAi Communications Co., Ltd.Natural Gas Industry B2352-85402023-08-01104361371Characterizing multi-scale shale pore structure based on multi-experimental imaging and machine learningJun Yao0Lei Liu1Yongfei Yang2Hai Sun3Lei Zhang4School of Petroleum Engineering, China University of Petroleum <East China>, Qingdao, Shandong, 266580, China; Research Centre of Multiphase Flow in Porous Media, China University of Petroleum <East China>, Qingdao, Shandong, 266580, ChinaSchool of Petroleum Engineering, China University of Petroleum <East China>, Qingdao, Shandong, 266580, China; Research Centre of Multiphase Flow in Porous Media, China University of Petroleum <East China>, Qingdao, Shandong, 266580, China; Corresponding author. School of Petroleum Engineering, China University of Petroleum <East China>, Qingdao, Shandong, 266580, China.School of Petroleum Engineering, China University of Petroleum <East China>, Qingdao, Shandong, 266580, China; Research Centre of Multiphase Flow in Porous Media, China University of Petroleum <East China>, Qingdao, Shandong, 266580, ChinaSchool of Petroleum Engineering, China University of Petroleum <East China>, Qingdao, Shandong, 266580, China; Research Centre of Multiphase Flow in Porous Media, China University of Petroleum <East China>, Qingdao, Shandong, 266580, ChinaSchool of Petroleum Engineering, China University of Petroleum <East China>, Qingdao, Shandong, 266580, China; Research Centre of Multiphase Flow in Porous Media, China University of Petroleum <East China>, Qingdao, Shandong, 266580, ChinaAn accurate and comprehensive understanding of shale pore structure is fundamental and critical for accurate reserves evaluation and efficient hydrocarbon development. Thus, by taking the shale of Paleogene Eocene Shahejie Formation in the Jiyang Depression, Bohai Bay Basin, as an example, the 2D and 3D multi-resolution images of the shale microstructure are obtained by multiple imaging technologies, including X-ray computed tomography, large-field scanning electron microscopy, scanning electron microscopy and focused ion beam scanning electron microscopy. By integrating image processing and machine learning algorithms, the shale pore structure is characterized at a single scale and multi scales. The results are obtained as follows. First, the shale pore space in the study area is mainly composed of microfractures, inorganic pores, organic matters and organic pores, and exclusively shows multi-scale characteristics. Second, there are various types of inorganic pores, and abundant dissolution pores; organic matters are distributed as strips and patches, and no organic pores are found in some organic matters. Third, pores with radius less than 20 nm account for 25%, those with radius between 20 and 50 nm account for 19%, those with radius between 50 and 100 nm account for 29%, those with radius between 100 and 500 nm account for 14%, those with radius between 500 nm and 20 μm account for 11%, and those with radius between 20 and 50 μm account for 2%. Fourth, the organic pores are less connected than the inorganic pores. The connectivity between organic pores and inorganic pores plays a crucial role in hydrocarbon migration, and microfractures control fluid flow channels. Fifth, pores with radius less than 50 nm are dominantly organic pores, those with radius between 50 and 500 nm are mainly organic and inorganic pores, and microfractures mainly contribute to the pores with radius more than 500 nm. It is concluded that a single imaging experiment cannot accurately and comprehensively reveal the multi-scale micro pore structure of a shale reservoir. Through integration of multiple imaging technologies and machine learning algorithms, the shale pore structure can be recognized and characterized at both single scale and multi scales. The proposed new method provides accurate and comprehensive information of multi-scale pore structures.http://www.sciencedirect.com/science/article/pii/S2352854023000475ShaleMulti-scaleMulti-typePore structureMulti-experimental imaging technologyMachine learning |
| spellingShingle | Jun Yao Lei Liu Yongfei Yang Hai Sun Lei Zhang Characterizing multi-scale shale pore structure based on multi-experimental imaging and machine learning Natural Gas Industry B Shale Multi-scale Multi-type Pore structure Multi-experimental imaging technology Machine learning |
| title | Characterizing multi-scale shale pore structure based on multi-experimental imaging and machine learning |
| title_full | Characterizing multi-scale shale pore structure based on multi-experimental imaging and machine learning |
| title_fullStr | Characterizing multi-scale shale pore structure based on multi-experimental imaging and machine learning |
| title_full_unstemmed | Characterizing multi-scale shale pore structure based on multi-experimental imaging and machine learning |
| title_short | Characterizing multi-scale shale pore structure based on multi-experimental imaging and machine learning |
| title_sort | characterizing multi scale shale pore structure based on multi experimental imaging and machine learning |
| topic | Shale Multi-scale Multi-type Pore structure Multi-experimental imaging technology Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2352854023000475 |
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