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 m...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.展开更多
以Aspen Open Solver接口集中的非线性代数方程组(NLA)部分作为研究对象,在对接口集进行系统地分析之后,利用AspenTech提供的接口代码将分别基于梯度和非基于梯度的四种求解算法嵌入生成solver组件,并实现用Aspen Plus调用该solver组件...以Aspen Open Solver接口集中的非线性代数方程组(NLA)部分作为研究对象,在对接口集进行系统地分析之后,利用AspenTech提供的接口代码将分别基于梯度和非基于梯度的四种求解算法嵌入生成solver组件,并实现用Aspen Plus调用该solver组件观察各种算法嵌入的结果。展开更多
文摘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.
