Novel brittleness index construction and pre-stack seismic prediction for gas hydrate reservoirs

被引:0
|
作者
Yang, Wenqiang [1 ,2 ,3 ]
Zong, Zhaoyun [1 ,2 ,3 ]
Liu, Xinxin [3 ,4 ]
Qin, Dewen [5 ]
Liu, Qingwen [5 ]
机构
[1] China Univ Petr East China, Natl Key Lab Deep Oil & Gas, Qingdao, Peoples R China
[2] China Univ Petr East China, Sch Geosci, Qingdao, Peoples R China
[3] Laoshan Lab, Qingdao, Peoples R China
[4] Qingdao Inst Marine Geol, Qingdao, Peoples R China
[5] CNOOC Shanghai, Shanghai, Peoples R China
基金
中国国家自然科学基金;
关键词
interpretation; parameter estimation; reservoir geophysics; rock physics; SHALE-GAS; FRACABILITY EVALUATION; ROCK BRITTLENESS; INVERSION; MODEL; IMPEDANCE;
D O I
10.1111/1365-2478.13628
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 ; 070902 ;
摘要
Reservoir transformation is essential for developing gas hydrate reservoirs. Predicting sediment brittleness is key to optimizing drilling design and evaluating engineering sweet spots. Constructing a brittleness index reflecting the brittle mineral content of a rock based on elastic parameters and predicting it using seismic data is a feasible solution for assessing reservoir brittleness. In addition, the elastic brittleness index can characterize the effect of complex pore types, fractures and pore fillings on rock brittleness. With the shallow hydrate reservoir in the sea as the research target. First, a novel brittleness index characterized by multiplying the Lam & eacute; parameter (lambda$\lambda $) by Poisson's ratio (sigma$\sigma $) is proposed. Its superiority in indicating brittle mineral content is verified by a rock-physics model. Second, a reflection coefficient approximation equation including the novel brittleness index is derived, enabling direct estimation of reservoir brittleness from seismic data. The new brittleness index has proven to better reflect brittle mineral content and effectively indicate the high brittleness characteristics of hydrate reservoirs. The accuracy of the proposed approximate equation is verified by a layered medium model, and the viability of predicting the new brittleness index using seismic data is also theoretically supported by the model test. Finally, the proposed method has obtained favourable results in the application of hydrate work area data collected at the South China Sea, confirming its availability and practicality.
引用
收藏
页码:380 / 396
页数:17
相关论文
共 50 条
  • [22] Prediction of shale gas preservation conditions by pre-stack geophysical technology: A case study of the shale gas reservoirs in the Jiaoshiba Block of the Sichuan Basin
    Zhang D.
    Sun W.
    Li S.
    Hao Y.
    Liu L.
    Natural Gas Industry, 2020, 40 (06) : 42 - 49
  • [23] An initial model construction method constrained by stratigraphic sequence representation for pre-stack seismic inversion
    Chen, Ting
    Zou, Bangli
    Wang, Yaojun
    Cai, Hanpeng
    Yu, Gang
    Hu, Guangmin
    GEOPHYSICAL PROSPECTING, 2024, 72 (07) : 2829 - 2843
  • [24] Impedance inversion of pre-stack seismic data in the depth domain
    Wei Jiang
    Xue Hua Chen
    Jie Zhang
    Xin Luo
    Zhi Wei Dan
    Wei Xiao
    Applied Geophysics, 2019, 16 : 427 - 437
  • [25] Brittleness index prediction in shale gas reservoirs based on efficient network models
    Shi, Xian
    Liu, Gang
    Cheng, Yuanfang
    Yang, Liu
    Jiang, Hailong
    Chen, Lei
    Jiang, Shu
    Wang, Jian
    JOURNAL OF NATURAL GAS SCIENCE AND ENGINEERING, 2016, 35 : 673 - 685
  • [26] Porosity prediction from pre-stack seismic data via a data-driven approach
    Yang, Naxia
    Li, Guofa
    Zhao, Pingqi
    Zhang, Jialiang
    Zhao, Dongfeng
    JOURNAL OF APPLIED GEOPHYSICS, 2023, 211
  • [27] Pre-stack seismic density inversion in marine shale reservoirs in the southern Jiaoshiba area, Sichuan Basin, China
    YuanYin Zhang
    ZhiJun Jin
    YeQuan Chen
    XiWu Liu
    Lei Han
    WuJun Jin
    Petroleum Science, 2018, 15 (03) : 484 - 497
  • [28] DIRECT PREDICTION METHOD OF FRACTURING ABILITY IN SHALE FORMATIONS BASED ON PRE-STACK SEISMIC INVERSION
    Wang, Chang
    Yin, Cheng
    Shi, Xuewen
    Pan, Shulin
    Gou, Qiyong
    Zhang, Dongjun
    Zeng, Chenwei
    Fang, Chunyang
    JOURNAL OF SEISMIC EXPLORATION, 2022, 31 (05): : 407 - 424
  • [29] A cyclic learning approach for improving pre-stack seismic processing
    Borges Oliveira, Dario Augusto
    Szwarcman, Daniela
    Ferreira, Rodrigo da Silva
    Zaytsev, Semen
    Semin, Daniil
    SCIENTIFIC REPORTS, 2021, 11 (01)
  • [30] A cyclic learning approach for improving pre-stack seismic processing
    Dario Augusto Borges Oliveira
    Daniela Szwarcman
    Rodrigo da Silva Ferreira
    Semen Zaytsev
    Daniil Semin
    Scientific Reports, 11