张冰^1,,
刘财¹,
郭智奇^1,,,
刘喜武^2,3,4,
刘宇巍^2,3,4
1. 吉林大学地球探测科学与技术学院, 长春 130026
2. 页岩油气富集机理与有效开发国家重点实验室, 北京 100083
3. 中国石化页岩油气勘探开发重点实验室, 北京 100083
4. 中国石化石油勘探开发研究院, 北京 100083

基金项目: 国家自然科学基金重点项目（41430322），国家十三五重大专项"陆相页岩油甜点地球物理识别与预测方法"课题（2017ZX05049-002）、国家自然科学基金石油化工联合基金（U1663207）联合资助


详细信息

作者简介: 张冰, 男, 1990年生, 博士研究生, 主要从事统计岩石物理、地震反演和算法方面的研究.E-mail:usrzhb@gmail.com

通讯作者: 郭智奇, 男, 1980年生, 博士, 教授、博士生导师, 主要从事岩石物理、地震各向异性正反演、油气储层预测等方面的研究.E-mail:guozhiqi@jlu.edu.cn

中图分类号: P631;P584

收稿日期:2017-02-13
修回日期:2018-03-29
上线日期:2018-06-05

Probabilistic reservoir parameters inversion for anisotropic shale using a statistical rock physics model

ZHANG Bing^1,,
LIU Cai¹,
Guo ZhiQi^1,,,
LIU XiWu^2,3,4,
LIU YuWei^2,3,4
1. College of GeoExploration Sicence and Technology, Jilin University, Changchun 130026, China
2. State Key Laboratory of Shale Oil and Gas Enrichment Mechanisms and Effective Development, Beijing 100083, China
3. SinoPEC Key Laboratory of Shale Oil/Gas Exploration and Production Technology, Beijing 100083, China
4. SinoPEC Petroleum Exploration and Production Research Institute, Beijing 100083, China


More Information

Corresponding author: Guo ZhiQi,E-mail:guozhiqi@jlu.edu.cn

MSC: P631;P584

--> Received Date: 13 February 2017
Revised Date: 29 March 2018
Available Online: 05 June 2018


摘要
摘要:提出了各向异性页岩储层统计岩石物理反演方法.通过统计岩石物理模型建立储层物性参数与弹性参数的定量关系，使用测井数据及井中岩石物理反演结果作为先验信息，将地震阻抗数据定量解释为储层物性参数、各向异性参数的空间分布.反演过程在贝叶斯框架下求得储层参数的后验概率密度函数，并从中得到参数的最优估计值及其不确定性的定量描述.在此过程中综合考虑了岩石物理模型对复杂地下介质的描述偏差和地震数据中噪声对反演不确定性的影响.在求取最大后验概率过程中使用模拟退火优化粒子群算法以提高收敛速度和计算准确性.将统计岩石物理技术应用于龙马溪组页岩气储层，得到储层泥质含量、压实指数、孔隙度、裂缝密度等物性，以及各向异性参数的空间分布及相应的不确定性估计，为页岩气储层的定量描述提供依据.
关键词: 储层描述/
各向异性/
岩石物理/
不确定性/
贝叶斯理论

Abstract:A stochastic inversion method of reservoir parameters for anisotropic shale is proposed by combing a rock physics model and Bayesian estimation. Quantitative relations between elastic parameters such as P-and S-wave impedances and reservoir petrophysical parameters including fracture and porosity are investigated using a statistical rock physics model. During the modeling, the error of rock physics model and noises in the seismic data are considered. In the process of estimating reservoir petrophysical parameters from elastic parameters, Bayesian inversion based on the statistical rock physics model is applicable to the uncertainty problem by giving the posterior probability distribution (PDF) of the unknown parameters. Then reservoir properties are obtained by the maximum a posteriori (MAP) criteria and associated uncertainty analysis. In the stochastic inversion, the SA-PSO algorithm which combines the simulated annealing method and the particle swarm optimization method shows its advantages in accuracy and efficiency. This method is applied to the Longmaxi Formation shale in China to obtain the sections of clay lamination (CL), clay content, porosity, fracture density and anisotropy parameters from given seismic sections of P-and S-wave impedances. The estimated reservoir parameters can be used for better characterizations of the sweet spots in shale reservoirs.
Key words:Reservoir characterization/
Anisotropy/
Rock physics/
Uncertainty/
Bayesian theory



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