Characterization of pores and microfractures in tight conglomerate reservoirs. (26th July 2022)

• Record Type: Journal Article
• Authors: Wang, Ziqiang; Ge, Hongkui; Zhou, Wei; Wei, Yun; Wang, Bei; Liu, Sai; Zhou, Hao; Du, Shuheng
• Is Part Of: International journal of hydrogen energy. Volume 47:Number 63(2022)
• Page Start: 26901
• Page End: 26914
• Publication Date: 2022-07-26
• Subjects: Mahu sag -- Tight conglomerate -- Pore -- Microfracture -- Characterization -- Machine learning; Hydrogen as fuel -- Periodicals; Hydrogène (Combustible) -- Périodiques; Hydrogen as fuel; Periodicals; 665.81
• DOI: 10.1016/j.ijhydene.2022.06.037
• Languages: English
• ISSNs: 0360-3199

Abstract

Abstract: 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. Graphical abstract: Image 1 Highlights: Pores and microfractures extractions by machine learning are used. Microfractures contribute more to energy storage and seepage than pores. Stress, cementation and corrosion control the complex geometric properties. Pores and microfractures are geometrically heterogeneous. The development of microfractures and pores are linked closely.