期刊
INTERNATIONAL JOURNAL OF HYDROGEN ENERGY
卷 47, 期 63, 页码 26901-26914出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ijhydene.2022.06.037
关键词
Mahu sag; Tight conglomerate; Microfracture; Characterization; Machine learning; Pore
资金
- National Natural Science Foundation of China (NSFC) [41902132, 12032019, 11872363]
This study used data-driven evaluation to investigate the internal control factors of pores and microfractures in tight conglomerate reservoirs. The results showed that microfractures dominate in fluid storage and seepage, while pores are more conducive to a homogeneous distribution of seepage flow and improved sweep efficiency.
This study aimed to carry out the data-driven evaluation of pores and microfractures in tight conglomerate reservoirs combining machine learning and complex geometric analysis, then investigate the internal control factors of reservoir damage.Results show that for the Upper Wuerhe formation of Mahu sag in Xinjiang of China, the average contribution rate of microfractures to fluid storage and seepage is 7.1 times that of pores, and microfractures dominate in fluid storage and seepage. Besides, the average contact probability between microfractures and fluids is 3.0 times that of pores. Compared with microfractures, pores are more conducive to form a homogeneous distribution of seepage flow and expand the sweep efficiency. On the contrary, microfracture is the dominant factor to aggravate the heterogeneity of seepage. The conclusions will provide crucial theoretical support and practical basis for the effective exploitation of tight conglomerate oil.(c) 2022 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
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