Improved estimation of elastic attributes from prestack seismic data for reservoir characterization

被引:1
作者
Zhou, Yijie [1 ,2 ]
Ruiz, Franklin [1 ]
Chen, Yequan [1 ]
Xia, Fan [1 ]
机构
[1] SINOPEC Tech Houston LLC, 3050 Post Oak Blvd,Suite 777, Houston, TX 77056 USA
[2] ION Geophys Corp, 2105 CityWest Blvd Suite 100, Houston, TX 77042 USA
关键词
MONTE-CARLO METHOD; ROCK-PHYSICS; INVERSION; POROSITY;
D O I
10.1190/GEO2019-0188.1
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
Seismic derivable elastic attributes, e.g., elastic impedance, lambda-rho, mu-rho, and Poisson impedance (PI), are routinely being used for reservoir characterization practice. These attributes could be derived from inverted V P, V S, and density, and usually indicate high sensitivity to reservoir lithology and fluid. Due to the high sensitivity of such elastic attributes, errors or measurement noise associated with the acquisition, processing, and inversion of prestack seismic data will propagate through the inversion products, and will lead to even larger errors in the computed attributes. To solve this problem, we have developed a two-step cascade workflow that combines linear inversion and nonlinear optimization techniques for the improved estimation of elastic attributes and better prediction and delineation of reservoir lithology and fluids. The linear inversion in the first step is an inversion scheme with a sparseness assumption, based on L1-norm regularization. This step is used to select the major reflective layer locations, followed in the second step by a nonlinear optimization process with the predefined layer structure. The combination of these two procedures produces a reasonable blocky earth model with consistent elastic properties, including the ones that are sensitive to reservoir lithology and fluid change, and thus provides an accurate approach for seismic reservoir characterization. Using PI, as one of the target elastic attributes, as an example, this workflow has been successfully applied to synthetic and field data examples. The results indicate that our workflow improves the estimation of elastic attributes from the noisy prestack seismic data and may be used for the identification of the reservoir lithology and fluid.
引用
收藏
页码:R41 / R53
页数:13
相关论文
共 50 条
[31]   Reservoir permeability estimation from seismic amplitudes using variational mode decomposition [J].
Xue, Ya-juan ;
Cao, Jun-xing ;
Wang, Xing-jian ;
Du, Hao-kun .
JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING, 2022, 208
[32]   Density stability estimation method from pre-stack seismic data [J].
Zong, Zhaoyun ;
Sun, Qianhao .
JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING, 2022, 208
[33]   Time-varying seismic wavelet estimation from nonstationary seismic data [J].
Feng Wei ;
Hu Tian-Yue ;
Yao Feng-Chang ;
Zhang Yan ;
Cui Yong-Fu ;
Peng Geng-Xin .
CHINESE JOURNAL OF GEOPHYSICS-CHINESE EDITION, 2017, 60 (01) :305-315
[34]   Reservoir porosity assessment and anomaly identification from seismic attributes using Gaussian process machine learning [J].
Putra, Maulana Hutama Rahma ;
Hermana, Maman ;
Yogi, Ida Bagus Suananda ;
Hossain, Touhid Mohammad ;
Abdurrachman, Muhammad Faris ;
Kadir, Said Jadid A. .
EARTH SCIENCE INFORMATICS, 2024, 17 (02) :1315-1327
[35]   Petrophysical parameters estimation of a reservoir using integration of wells and seismic data: a sandstone case study [J].
Leisi, Ahsan ;
Saberi, Mohammad Reza .
EARTH SCIENCE INFORMATICS, 2023, 16 (01) :637-652
[36]   Adding geologic prior knowledge to Bayesian lithofluid facies estimation from seismic data [J].
Gonzalez, Ezequiel F. ;
Gesbert, Stephane ;
Hofmann, Ronny .
INTERPRETATION-A JOURNAL OF SUBSURFACE CHARACTERIZATION, 2016, 4 (03) :SL1-SL8
[38]   Use of genetic algorithm in reservoir characterisation from seismic data: A case study [J].
Maurya, Satya Prakash ;
Singh, Nagendra Pratap ;
Singh, Kumar Hemant .
JOURNAL OF EARTH SYSTEM SCIENCE, 2019, 128 (05)
[39]   Estimation of rock physics properties from seismic attributes - Part 1: Strategy and sensitivity analysis [J].
Dupuy, Bastien ;
Garambois, Stephane ;
Virieux, Jean .
GEOPHYSICS, 2016, 81 (03) :M35-M53
[40]   Bayesian estimation of reservoir properties-effects of uncertainty quantification of 4D seismic data [J].
Eikrem, Kjersti Solberg ;
Naevdal, Geir ;
Jakobsen, Morten ;
Chen, Yan .
COMPUTATIONAL GEOSCIENCES, 2016, 20 (06) :1211-1229