基于超球体变换EnKF方法的油藏动态历史拟合研究

发布时间：2018-05-07 07:49

本文选题：油藏动态历史拟合 + EnKF　；参考：《重庆科技学院》2017年硕士论文

【摘要】：油藏动态历史拟合是油田预测开发动态的基础,也是油藏闭合生产优化的重要组成部分之一。依靠油藏工程师主观认识的传统人工历史拟合方法,已经不能满足现代油藏描述精细化、复杂化以及定量化的要求。借助于现代信息技术与智能技术的发展,需寻求一种拟合速度更快、精度更高动态历史拟合方法,来降低油藏地质参数的不确定性、提高对油层地下流体分布及油藏未来动态变化的认识,确保油水井的高效生产。本文在总结分析国内外动态历史拟合研究的基础上,提出一种基于超球体变换集合卡尔曼滤波(Super-sphere Transform Ensemble Kalman Filter,SEnKF)方法的油藏动态历史拟合方法。本文的主要研究内容包括:(1)基于正态得分集合卡尔曼滤波(Normal Score Transformation Ensemble Kalman Filter,NS-EnKF)方法的油藏动态历史拟合研究。该方法针对渗透率场呈非高斯分布的特性,引入正态得分变换,将其转换为高斯分布,输入到模拟器中对油藏的生产状态进行预测;然后根据观测的生产值对状态进行更新;最后利用正态得分逆变换将渗透率转换到原来的分布空间中,保证其分布的原始特性。对比EnKF与NS-EnKF两种算法的仿真结果,NS-EnKF算法的反演效果较好,能够更准确的描述地质参数。(2)基于超球体变换正态得分EnKF算法的油藏动态历史拟合研究。考虑到算法的精度,利用超球体变换构造简单、误差小、易于实现等优点,对NS变换后的渗透率场进行超球体变换。同时,引入协方差矩阵局部化(localization of covariance,LC)方法来避免集合更新过程中可能出现的滤波发散问题。以此获得更多的统计信息,提高历史拟合精度,降低油藏参数的不确定性。(3)利用经典的二维四采一注模型进行模拟仿真,验证算法的可操作性和有效性,同时,采用Norne油田的实际地质参数进行算法的对比分析。结果表明正态得分的引入较好的解决了油藏参数的非高斯问题;LC技术、超球体变换与EnKF的耦合避免了小集合数目时的滤波发散,并改善了模型参数反演效果,为油藏工程师对地质流体进行精细描述提供了更精确的参考依据。
[Abstract]:Reservoir performance history fitting is the basis of oil field development performance prediction, and also one of the important components of reservoir closed production optimization. The traditional artificial history fitting method based on the subjective knowledge of reservoir engineers can no longer meet the requirements of modern reservoir description, such as refinement, complexity and quantification. With the development of modern information technology and intelligent technology, it is necessary to find a method of faster fitting speed and higher precision dynamic historical fitting to reduce the uncertainty of reservoir geological parameters. To improve the understanding of the underground fluid distribution and reservoir performance in the future to ensure the efficient production of oil and water wells. On the basis of summing up and analyzing the research of dynamic history fitting at home and abroad, this paper presents a reservoir history fitting method based on hypersphere transform set Kalman filter and Super-sphere Transform Ensemble Kalman filter (SEnKF) method. The main research contents of this paper include: (1) Reservoir history fitting based on normal Score Transformation Ensemble Kalman filter NS-EnKF (normal Score Transformation Ensemble Kalman filter) method based on normal score set. According to the non- distribution of permeability field, this method introduces normal score transform and converts it to Gao Si distribution, then inputs it to the simulator to predict the production state of the reservoir, and then updates the state according to the observed production value. Finally, the permeability is transformed into the original distribution space by the inverse transformation of normal score to ensure the original characteristics of the distribution. Compared with the simulation results of EnKF and NS-EnKF, NS-EnKF algorithm has a better inversion effect and can describe geological parameters more accurately. The research of reservoir dynamic history fitting based on EnKF algorithm of normal score of hypersphere transformation is presented. Considering the accuracy of the algorithm, using the advantages of simple structure, small error and easy realization of hypersphere transformation, the permeability field after NS transformation is transformed by hypersphere. At the same time, the covariance matrix localization of covarianceL method is introduced to avoid the filtering divergence problem which may occur in the course of set updating. In order to obtain more statistical information, to improve the accuracy of historical fitting, to reduce the uncertainty of reservoir parameters, and to use the classical two-dimensional four-production-one-injection model to simulate and simulate the algorithm, the maneuverability and effectiveness of the algorithm are verified, at the same time, The actual geological parameters of Norne oilfield are compared and analyzed. The results show that the introduction of normal score can solve the non-Gao Si problem of reservoir parameters well. The coupling of hypersphere transformation and EnKF avoids the filter divergence when the number of sets is small, and improves the inversion effect of model parameters. It provides a more accurate reference for reservoir engineers to describe geological fluids.
【学位授予单位】：重庆科技学院
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TE331

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