摘要
无论是迭代类随机反演还是线性贝叶斯随机反演,一般采用序贯类随机模拟表征油藏的非均质性。序贯类方法大多依赖变差函数或训练图像描述模型参数的空间相关性,需要逐点计算模拟结果,因此并行实现困难、计算效率低。为此,将一种条件化快速傅里叶变换滑动平均(FFT⁃MA)模拟引入线性反演框架,提出基于空间协模拟的叠前地震随机反演方法。首先,在贝叶斯框架下整合地震数据和测井低频信息,获得弹性参数的后验概率分布。然后,根据FFT⁃MA算法生成概率场,以测井数据为条件数据对贝叶斯后验应用概率场协同模拟,从而获得井震数据约束的叠前弹性参数高分辨率随机反演结果。该方法无需迭代更新模型参数,能极大地提高随机反演的计算效率。最后,由模型试算和实际资料应用测试方法的效果。模型试算表明,所提方法在高分辨率储层预测及计算效率方面均优于常规方法,可稳定、准确地描述小尺度储层特征。实际资料应用表明,所提方法得到的高分辨反演结果与测井数据契合良好,提高了随机反演在薄储层定量表征方面的实用性。
Sequential stochastic simulation is generally used to characterize reservoir heterogeneity both in the iterative stochastic inversion and linear Bayesian stochastic inversion.Most sequential simulation methods rely on variograms or training images to describe the spatial correlation of model parameters.In addition,the simula⁃tion results are required to be calculated point by point,which makes parallel computing difficult and reduces computational efficiency.Therefore,a conditioned fast Fourier transform moving average(FFT⁃MA)is intro⁃duced into the linear inversion framework,and a prestack seismic stochastic inversion method based on spatial co⁃simulation is proposed.Firstly,the posterior probability distribution of elastic parameters is obtained by inte⁃grating seismic data and low⁃frequency well logging information under the Bayesian framework.Then,the probability field is generated according to the FFT⁃MA algorithm.Well logging data is taken as conditional data to conduct Bayesian posterior probability field co⁃simulation.High⁃resolution prestack stochastic inversion results of elastic parameters constrained by well logging and seismic data are thus obtained.No iteration and up⁃date of model parameters is required by the method,which greatly improves the computational efficiency of sto⁃chastic inversion.Finally,the validity of the proposed method is demonstrated by numerical model examples and practical data application cases.The numerical model examples show that the proposed method has signifi⁃cant advantages over conventional methods in terms of high⁃resolution reservoir prediction and computational ef⁃ficiency.Small⁃scale reservoir characterizations can be explored stably and accurately.The practical data appli⁃cation cases show that the high⁃resolution inversion results obtained by the proposed method match well with well logging data.The practicality of stochastic inversion in characterizing quantitatively thin reservoirs is greatly improved.
作者
曹亚梅
周辉
于波
张元高
戴世立
CAO Yamei;ZHOU Hui;YU Bo;ZHANG Yuangao;DAI Shili(National Key Laboratory of Petroleum Resources and Engineering,China University of Petroleum(Beijing),Beijing 102249,China;CNPC Key Laboratory of Geophysical Exploration,China University of Petroleum(Beijing),Beijing 102249,China;School of Earth Sciences,Northeast Petroleum University,Daqing,Heilongjiang 163318,China;Exploration Department of Daqing Oilfield Company Ltd,Daqing,Heilongjiang 163453,China)
出处
《石油地球物理勘探》
EI
CSCD
北大核心
2024年第2期268-278,共11页
Oil Geophysical Prospecting
基金
国家重点研发计划项目“多信息相容约束高效全波形反演方法研究”(2018YFA0702502)
中国石油天然气集团有限公司“十四·五”项目“物探应用基础实验和前沿理论方法研究”(2022DQ0604-04)
中国石油天然气集团有限公司前瞻性基础性项目“物探岩石物理与前沿储备技术研究”(2021DJ3506)联合资助。
