Genetic algorithm application for matching ordinary black oil PVT data

查看全文 作　　者：Mohammad Taghizadeh [1]Sarvestani;Behnam Sedaee [2]Sola;Fariborz [3]Rashidi

机构地区：[1]Department of Petroleum Engineering, Amirkabir University of Technology, Tehran, Iran;[2]Institute of Petroleum Engineering, University of Tehran, Tehran, Iran;[3]Department of Chemical Engineering, Amirkabir University of Technology, Tehran, Iran

出　　处：《Petroleum Science》索引2012年第9卷第2期,共13页

摘　　要：In the study of reservoirs, it is vital that we have a realistic physical model of the reservoir fluid that accurately describes the hydrocarbon system and its properties. The available equations of state (EOS) to model the fluid phase behavior have some inherent deficiencies that may cause erroneous predictions for real reservoir fluids, so these models should be tuned against experimental data by adjusting some parameters. Since there are many matching parameters, tuning the EOS against experimental data is a tedious and difficult work. In this study, a genetic algorithm as an optimization technique is used to solve this regression problem. This study presents a new method that uses a specially designed genetic algorithm to search for suitable regression parameters to match the EOS against measured data. The proposed method has been tested on three real black oil samples. The results show the surprising performance of the developed genetic algorithm to match the experimental data of the selected fluid samples. The main advantage of the used method is its high speed in finding a solution. Also, finding more than one solution, working automatically, confining the role of experts to the last stage, reducing costs and having the possibility of evaluating the different situations are the other advantages of this method to match ordinary black oil PVT data and makes it an ideal method to implement as an automatic EOS tuning algorithm for black oils. 关键词：PVT数据遗传算法匹配参数黑油调整算法应用储层流体实验数据

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Genetic algorithm application for matching ordinary black oil PVT data

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