Progress of the research on productivity prediction methods for stimulated reservoir volume (SRV)-fractured horizontal wells in unconventional hydrocarbon reservoirs

被引:11
|
作者
Ren, Long [1 ,2 ]
Su, Yuliang [3 ]
Zhan, Shiyuan [3 ]
Meng, Fankun [4 ]
机构
[1] Xian Shiyou Univ, Sch Petr Engn, Xian 710065, Shaanxi, Peoples R China
[2] Shaanxi Key Lab Adv Stimulat Technol Oil & Gas Re, Xian 710065, Shaanxi, Peoples R China
[3] China Univ Petr East China, Sch Petr Engn, Qingdao 266580, Peoples R China
[4] PetroChina Res Inst Petr Explorat & Dev, Beijing 100083, Peoples R China
基金
中国国家自然科学基金;
关键词
Unconventional hydrocarbons; SRV fracturing; Horizontal well; Productivity prediction; Theoretical method; HYDRAULIC FRACTURE; PROPAGATION; MODEL; PERFORMANCE;
D O I
10.1007/s12517-019-4376-2
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
Further development of stimulated reservoir volume (SRV)-fracturing technologies for horizontal wells is necessary to address the current situation of decreasing energy production from conventional sources and continuous increases in the proportion of alternative energy sources (unconventional hydrocarbons). To this end, the theoretical productivity prediction methods used for SRV-fractured horizontal wells must be studied. In this article, based on a structural description of hydraulic fracture networks around horizontal wells, we summarize and evaluate various types of productivity prediction models built by researchers using analytical, semi-analytical, and numerical methods. We find that while these models are convenient and efficient, analytical models are overly ideal and differ considerably from actual fracture network patterns. We also find that semi-analytical models offer high computational accuracy but are applicable only to the prediction of single-well single-phase productivity. Furthermore, numerical models can address more complex fracture network patterns and can be used to calculate the multi-phase productivity of well patterns but involve a complex initial input process. On this basis, we summarize the problems and challenges associated with the existing productivity prediction models and specify five pertinent key techniques involved in building more accurate productivity prediction models: the techniques for describing complex fracture network structures; the technique for simulating multi-phase flows in reservoirs; the multi-scale multi-field flow coupling theory; the technique for calculating numerical solutions for complex models; and the technique for matching well type, well pattern, and fracture network systems.
引用
收藏
页数:15
相关论文
共 26 条
  • [21] Rate Transient Analysis for Multistage Fractured Horizontal Well in Tight Oil Reservoirs considering Stimulated Reservoir Volume
    Jiang, Ruizhong
    Xu, Jianchun
    Sun, Zhaobo
    Guo, Chaohua
    Zhao, Yulong
    MATHEMATICAL PROBLEMS IN ENGINEERING, 2014, 2014
  • [22] A productivity prediction method for multi-fractured horizontal wells in tight oil reservoirs considering fracture closure
    Gao, Xinchen
    Guo, Kangliang
    Chen, Peng
    Yang, Haoran
    Zhu, Guowei
    JOURNAL OF PETROLEUM EXPLORATION AND PRODUCTION TECHNOLOGY, 2023, 13 (03) : 865 - 876
  • [23] A productivity prediction method for multi-fractured horizontal wells in tight oil reservoirs considering fracture closure
    Xinchen Gao
    Kangliang Guo
    Peng Chen
    Haoran Yang
    Guowei Zhu
    Journal of Petroleum Exploration and Production Technology, 2023, 13 : 865 - 876
  • [24] Nonlinear flow model of multiple fractured horizontal wells with stimulated reservoir volume including the quadratic gradient term
    Ren, Junjie
    Guo, Ping
    JOURNAL OF HYDROLOGY, 2017, 554 : 155 - 172
  • [25] Pressure performance of multi-stage fractured horizontal well with stimulated reservoir volume and irregular fractures distribution in shale gas reservoirs
    Xu, Youjie
    Li, Xiaoping
    Liu, Qiguo
    JOURNAL OF NATURAL GAS SCIENCE AND ENGINEERING, 2020, 77
  • [26] An Analytical Solution for Transient Productivity Prediction of Multi-Fractured Horizontal Wells in Tight Gas Reservoirs Considering Nonlinear Porous Flow Mechanisms
    Wang, Qiang
    Wan, Jifang
    Mu, Langfeng
    Shen, Ruichen
    Jurado, Maria Jose
    Ye, Yufeng
    ENERGIES, 2020, 13 (05)