Analysis of Differences and Main Controlling Factors of the Chang 8 Reservoir in the Longdong-Jiyuan Area
DOI:
https://doi.org/10.54691/sz48ya65Keywords:
Ordos Basin; Chang 8 Reservoir; Reservoir Characteristics; Development Performance; Grey Correlation Analysis; Main Controlling Factors.Abstract
Although the Chang 8 reservoir in the Ordos Basin holds significant resource potential as a crucial exploration and development interval, existing research has primarily focused on regional geological patterns, with limited systematic analysis of reservoir characteristics and production performance. To address this gap, this study investigates the Longdong and Jiyuan areas by integrating data on petrological characteristics, physical properties, pore structures, percolation features, and production performance. A systematic comparison of reservoir differences between the two regions was conducted, and the grey correlation method was employed to identify the main controlling factors influencing development performance. The results indicate that the Longdong area has a relatively simple sediment source, with lithologies dominated by lithic feldspathic sandstones. The interstitial materials are primarily chlorite, which aids in preserving primary intergranular pores, resulting in an ultra-low permeability reservoir overall. In contrast, the Jiyuan area, influenced by multiple sediment sources, exhibits complex rock types with higher kaolinite content in interstitial materials and well-developed feldspar dissolution pores, yet it displays even lower permeability, classifying it as a super-low permeability reservoir. Regarding pore structure, the Longdong area features a favorable pore-throat configuration, characterized by low displacement pressure and good connectivity, whereas the Jiyuan area is dominated by fine pore throats with complex structures. Water flooding experiments reveal that although the Jiyuan area demonstrates higher oil displacement efficiency during both the anhydrous period and the final stage, it experiences a higher decline rate and slightly lower initial production compared to the Longdong area. Grey correlation analysis shows that the main controlling factors for initial productivity are intergranular pores, maximum mercury saturation, and porosity; for the decline rate, the key factors include final oil displacement efficiency, feldspar dissolution pores, injection pore volume multiples, and porosity. Comprehensive analysis suggests that differences in sediment sources control the reservoir rock and pore compositions, thereby influencing percolation capacity and development response characteristics. These findings provide a theoretical basis for efficient development and precise regulation of Chang 8 reservoirs.
Downloads
References
[1] Chen B ,Zhang R ,Sun Q , et al. Diagenetic Characteristics and Pore Evolution of Low-Permeability Sandstone Reservoirs: A Case Study of Chang 2 Reservoir in Jiyuan Area, Ordos Basin.[J].ACS omega, 2025, 10(16):16044-16060.DOI:10.1021/ACSOMEGA.4C09143.
[2] Jiang M ,Chen D ,Wang Q , et al. Occurrence Mechanism of Crude Oil Components in Tight Reservoirs: A Case Study of the Chang 7 Tight Oil in the Jiyuan Area, Ordos Basin, China[J]. Energies, 2025, 18 (6): 1440-1440.DOI:10.3390/EN18061440.
[3] Wang J ,Wang L ,Yin Y , et al. Comprehensive Reservoir Architecture Dissection and Microfacies Analysis of the Chang 8 Oil Group in the Luo 1 Well Area, Jiyuan Oilfield[J].Applied Sciences, 2025, 15 (3):1082-1082.DOI:10.3390/APP15031082.
[4] Quanpei Z ,Hongpeng Q ,Yong H , et al. Study of pore-throat structure characteristics and fluid mobility of Chang 7 tight sandstone reservoir in Jiyuan area, Ordos Basin[J].Open Geosciences, 2023, 15(1):DOI:10.1515/GEO-2022-0534.
[5] Song X ,Gao H ,Feng C , et al. Analysis of the Influence of Micro-Pore Structure on Oil Occurrence Using Nano-CT Scanning and Nuclear Magnetic Resonance Technology: An Example from Chang 8 Tight Sandstone Reservoir, Jiyuan, Ordos Basin[J].Processes, 2023,11(4): DOI: 10. 3390/ PR11 041127.
[6] Zhou R ,Zhou J, Zhou K , et al. Research on gray correlation evaluation method for ultrasonic cavitation anti-cavitation performance of epoxy resin materials[J].Construction and Building Materials,2026,516145691-145691.DOI:10.1016/J.CONBUILDMAT.2026.145691.
[7] Chen Q ,Meng P ,Hu X , et al. Optimization of the extraction process of Sanhuang Qingre Formula by integrating response surface methodology, grey correlation analysis, and machine learning.[J]. Scientific reports,2026,16(1):6767-6767.DOI:10.1038/S41598-026-37751-0.
[8] Zhou Y ,Xi L ,Xu L , et al. Thermal performance analysis and multi-objective multi-method collaborative optimization of orthogonal rib-enhanced blade leading edge jet array impingement cooling[J].Aerospace Science and Technology,2026,170111570-111570.DOI:10.1016/J. AST. 2025. 111570.
[9] Qi Y ,Cheng Q ,Li S , et al. Grey correlation analysis of the main influencing factors of wax deposition of waxy crude oil and determination of pigging period based on dynamic analysis[J].Petroleum Science and Technology,2025,43(24):3607-3625.DOI:10.1080/10916466.2024.2423038.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Scientific Journal of Technology

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.






