| 基于计算岩石物理方法的页岩储层弹性参数提取 |
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| 引用本文： | 霍志周,刘喜武,刘宇巍,张剑锋,高红伟,王劲松.基于计算岩石物理方法的页岩储层弹性参数提取[J].地球物理学报,2018,61(7):3019-3027. |
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| 作者姓名： | 霍志周 刘喜武 刘宇巍 张剑锋 高红伟 王劲松 |
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| 作者单位： | 1. 中国石油化工股份有限公司石油勘探开发研究院, 北京 100083;2. 中国科学院地质与地球物理研究所, 北京 100029 |
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| 基金项目： | 国家重大专项（2017ZX05049002）、国家自然科学基金委员会-中国石油化工股份有限公司石油化工联合基金项目（U1663207）和国家重点基础研究发展计划（973）项目（2014CB239104）资助. |
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| 摘 要： | 发展了应用数值计算方法获取页岩储层的速度、各向异性参数的计算岩石物理系列方法.该系列方法包括了大尺度精细地质模型数值建模、计算网格尺度的地球物理建模和地震波数值模拟提取岩石物理弹性参数.本文方法利用储层的统计数据而不是具体岩心的测量数据，可获得储层岩石物理弹性参数的变化规律.相比于基于岩心测试的岩石物理方法，本文方法可精细考虑实际储层的非均匀特征，可得到岩心测试难以求取的与尺寸效应高度相关的弹性参数，也避免了求取弹性参数变化规律时获取不同地质特征岩心的困难.本文发展了计算岩石物理方法，为计算岩石物理面临的大尺度地质建模和计算能力限制问题提供了有效的解决方案.文中以胜利罗家的页岩储层为例，求得了储层TOC含量从3%到21%变化情况下储层的P波、S波速度以及各向异性参数变化规律.
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| 关 键 词： | 页岩 计算岩石物理 储层弹性参数 |
| 收稿时间： | 2017-10-27 |
Determining elastic parameters of shale reservoirs based on computational rock physics methods |
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| HUO ZhiZhou,LIU XiWu,LIU YuWei,ZHANG JianFeng,GAO HongWei,WANG JinSong.Determining elastic parameters of shale reservoirs based on computational rock physics methods[J].Chinese Journal of Geophysics,2018,61(7):3019-3027. |
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| Authors: | HUO ZhiZhou LIU XiWu LIU YuWei ZHANG JianFeng GAO HongWei WANG JinSong |
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| Institution: | 1. SINOPEC Exploration & Production Research Institute, Beijing 100083, China;2. Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China |
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| Abstract: | A series of computational rock physics methods to obtain velocity and anisotropic parameters of shale reservoirs have been developed. This series of methods includes numerical modeling of large-scale refined geological models, grid-scale geophysical modeling and determining petrophysical parameters through numerical simulation of seismic waves. The methods proposed in this paper use statistical data of reservoirs rather than measurement data from cores to get varying patterns of reservoir rock physics elastic parameters. Compared to the rock physics method based on core measurement, this method can carefully consider the inhomogeneous characteristics of reservoirs, obtain the elastic parameters high related to the size effect which are difficult to acquire by core measuring, and can also avoid the difficulty of obtaining cores with different geological features when getting varying patterns of elastic parameters. In sum, a series of computational rock physical methods have been developed in this work, which provides an effective solution to the problem of large scale geological modeling and computational capacity limitation. Taking the Luojia, Shengli shale reservoir as an example, P wave velocity, S wave velocity and anisotropy parameters of this reservoir varying with TOC content from 3% to 21% are obtained using this series of methods. |
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| Keywords: | Shale Computational rock physics Reservoir elastic parameters |
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