刘兴业^1,2,,
李景叶^1,2,,,
陈小宏^1,2,
李超^1,2,
郭康康^1,2,
周林^1,2
1. 中国石油大学(北京) 油气资源与探测国家重点实验室, 北京 102249
2. 中国石油大学(北京) 海洋石油勘探国家工程实验室, 北京 102249

基金项目: 国家自然科学基金（41774129，41774131），国家科技重大专项课题（2016ZX05033003-008，2016ZX05024001-004）和中国石油天然气集团公司科技项目（2016A-3303）联合资助


详细信息

作者简介: 刘兴业, 男, 1991年生, 在读博士生, 研究方向为油藏地球物理与地质统计学.E-mail:lwxwyh506673@126.com

通讯作者: 李景叶, 男, 1978年生, 教授, 博士生导师, 2005年获中国石油大学(北京)博士学位, 研究方向为储层地球物理.E-mail:ljy3605@sina.com

中图分类号: P631

收稿日期:2017-09-01
修回日期:2018-03-26
上线日期:2018-07-05

A stochastic inversion method integrating multi-point geostatistics and sequential Gaussian simulation

LIU XingYe^1,2,,
LI JingYe^1,2,,,
CHEN XiaoHong^1,2,
LI Chao^1,2,
GUO KangKang^1,2,
ZHOU Lin^1,2
1. State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum, Beijing 102249, China
2. National Engineering Laboratory for Offshore Oil Exploration, China University of Petroleum, Beijing 102249, China


More Information

Corresponding author: LI JingYe,E-mail:ljy3605@sina.com

MSC: P631

--> Received Date: 01 September 2017
Revised Date: 26 March 2018
Available Online: 05 July 2018


摘要
摘要:岩相和储层物性参数是油藏表征的重要参数，地震反演是储层表征和油气藏勘探开发的重要手段.随机地震反演通常基于地质统计学理论，能够对不同类型的信息源进行综合，建立具有较高分辨率的储层模型，因而得到广泛关注.其中，概率扰动方法是一种高效的迭代随机反演策略，它能综合考虑多种约束信息，且只需要较少的迭代次数即可获得反演结果.在概率扰动的优化反演策略中，本文有效的联合多点地质统计学与序贯高斯模拟，并结合统计岩石物理理论实现随机反演.首先，通过多点地质统计学随机模拟，获得一系列等可能的岩相模型，扰动更新初始岩相模型后利用相控序贯高斯模拟建立多个储层物性参数模型；然后通过统计岩石物理理论，计算相应的弹性参数；最后，正演得到合成地震记录并与实际地震数据对比，通过概率扰动方法进行迭代，直到获得满足给定误差要求的反演结果.利用多点地质统计学，能够更好地表征储层空间特征.相控序贯高斯模拟的应用，能够有效反映不同岩相中储层物性参数的分布.提出的方法可在较少的迭代次数内同时获得具有较高分辨率的岩相和物性参数反演结果，模型测试和实际数据应用验证了方法的可行性和有效性.
关键词: 随机反演/
多点地质统计学/
概率扰动方法/
序贯高斯模拟/
储层表征

Abstract:Facies and reservoir properties are important parameters to characterize reservoirs. Seismic inversion has become one of the most widely used techniques in reservoir modeling, characterization and prediction. Stochastic inversion based on geostatistical theory can integrate different information and data and permits to establish reservoir models with high resolution. Of it, the probability perturbation method, which is an optimized process by iteration, is an effective strategy to solve inverse problems. It can integrate different probabilistic information into a joint probability by the Tau model and can produce invertion results in a small number of iterations.
Based on the probability perturbation method, we integrate multi-point statistics simulation and sequential Gaussian simulation to form a new strategy for inverting facies and reservoir properties. A series of models of facies are generated by conditional simulation with multi-point geostatistics at first. Multi-point geostatistics allows considering the spatial correlation among multiple points and can produce fine-scaled facies models. Then, perturbing the probability distribution gains a new facies model. Next, several models of reservoir properties are established by using facies-controlled sequential Gaussian simulation. Compared with the direct sequential Gaussian simulation, facies-controlled sequential Gaussian simulation can better characterize the spatial continuity of reservoir properties in different facies because it analyzes the variograms for different lithofacies. Finally, elastic attributes are computed by statistical rock physics, synthetic seismograms are computed by forward modeling and these seismograms are matched with actual seismic data. This method allows obtaining models of facies and reservoir properties simultaneously in a relatively small number of iterations. To demonstrate the feasibility and effectiveness of the method, we test it on the Stanford Ⅵ model. Then, the method is applied to a seismic section from a region of China.
Key words:Stochastic inversion/
Multi-point geostatistics/
Probability perturbation method/
Sequential Gaussian simulation/
Reservoir characterization



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