摘要
以往针对富有机质岩石横波速度预测方法大多未同时考虑干酪根分布和孔隙形状的影响,为此,基于Kuster-Toksoz(KT)岩石物理模型和混沌量子粒子群寻优算法构建了一种新的富有机质岩石横波速度预测方法:把富有机质岩石等效为由矿物、干酪根颗粒、含流体孔隙组成的混合物,其中干酪根颗粒与孔隙同时被等效为硬币形状包含物,通过其纵横比变化表征干酪根分布及孔隙形状对速度的影响;使用预测与实测纵波速度之间的误差构建反演目标函数,引入混沌量子粒子群非线性多元全局寻优算法同时求解等效干酪根颗粒和等效孔隙纵横比参数,在反演参数的基础上预测横波速度。将该方法应用于实验室测量数据和实际测井数据,并与前人提出的三种单一参数自适应方法进行对比,结果显示新方法优于单一参数自适应方法,证明新方法在富有机质岩石横波速度预测中有效。
Aiming at the disadvantage that the influence of kerogen distribution and pore structure on velocity is not taken into account in the rock physics model of organic-rich rock,we present a method for S-wave velocity prediction of organic-rich rock by integrating the rock physics model(KusterToks9 z)with a nonlinear global optimization algorithm.In this method,the organic-rich rock is equivalent to a mixture of minerals,kerogen particles and fluid-containing pores,in which kerogen particles and pores are both equivalent to ellipsoidshaped inclusions.The effect of kerogen distribution and pore shape on S-wave velocity is described according to the change of the aspect ratio of ellipsoids.The error between the predicted and measured P-wave velocities is applied to establish the inverse objective function.Then calculate two parameters,the equivalent kerogen particles and the equivalent pore aspect ratios,by the optimization algorithm.The S-wave velocity is predicted based on the inverted parameters.Compared with three single-adaptive parameter methods commonly used in the industry,the new method of S-wave velocity prediction based on kerogen and pore aspect ratios simultaneously inverted from P-wave velocity(or P-and S-wave velocities)is more effective.
作者
刘致水
刘俊州
董宁
包乾宗
王震宇
时磊
LIU Zhishui;LIU Junzhou;DONG Ning;BAO Qianzong;WANG Zhenyu;SHI Lei(College of Geology Engineering and Geomatics,Chang*an University,Xi'an,Shaanxi 710054,China;Research Institute of Petroleum Exploration and Development,SINOPEC,Beijing 100083,China)
出处
《石油地球物理勘探》
EI
CSCD
北大核心
2021年第1期127-136,154,I0012,共12页
Oil Geophysical Prospecting
基金
国家科技重大专项“碎屑岩储层岩石物理建模与地震成像方法研究”(2016ZX05002-005-002)
中央高校基本科研业务费专项资金资助项目“群集智能优化含油气页岩岩石物理速度预测系统研究”(300102268103)、“基于压缩感知的地震数据采集和波形反演方法研究”(310826172002)
中国石油化工股份有限公司科技部项目“薄储层提高分辨率处理与流体识别技术研究”(PE19008-2)联合资助。
