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Application of Computer Streamline Simulation Technique in Oilfield

Daiyin Yin, Huan Wang

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Abstract

With the rapid development of computer, applied mathematics and reservoir engineering, which have provided a technology platform for the scientific management, digitalization and informationization of reservoir, the integration research of geological modeling and numerical simulation is more and more emphasized in oilfield. In this paper, the mathematical theory of streamline simulation is given. This method has high computational speed and strong convergence, and it can be used to select the optimal model from random geological models quickly. In this article, taking Xing-5 region of Daqing Oilfield as an example, three equal probability models are established by sequential Gaussian random simulation technique, and the streamline simulation for the three models is carried out. It reproduces the historical flow performance of liquid in the reservoir. The three equal probability random simulation models have been selected optimally. Simulation results show that the stochastic geological model 1 is the best one and its streamlines chart is given. It is easy to see the direction and amount of fluid flowing from injectors to producers with streamlines, and the connectivity between injectors and producers is also identified with high accuracy.

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What this paper is about

With the rapid development of computer, applied mathematics and reservoir engineering, which have provided a technology platform for the scientific management, digitalization and informationization of reservoir, the integration research of geological modeling and numerical simulation is more and more emphasized in oilfield. In this paper, the mathematical theory of streamline simulation is given. This method has high computational speed and strong convergence, and it can be used to select the optimal model from random geological models quickly. In this article, taking Xing-5 region of Daqing Oilfield as an example, three equal probability models are established by sequential Gaussian random simulation technique, and the streamline simulation for the three models is carried out. It reproduces the historical flow performance of liquid in the reservoir. The three equal probability random simulation models have been selected optimally. Simulation results show that the stochastic geological model 1 is the best one and its streamlines chart is given. It is easy to see the direction and amount of fluid flowing from injectors to producers with streamlines, and the connectivity between injectors and producers is also identified with high accuracy.

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Available abstract

With the rapid development of computer, applied mathematics and reservoir engineering, which have provided a technology platform for the scientific management, digitalization and informationization of reservoir, the integration research of geological modeling and numerical simulation is more and more emphasized in oilfield. In this paper, the mathematical theory of streamline simulation is given. This method has high computational speed and strong convergence, and it can be used to select the optimal model from random geological models quickly. In this article, taking Xing-5 region of Daqing Oilfield as an example, three equal probability models are established by sequential Gaussian random simulation technique, and the streamline simulation for the three models is carried out. It reproduces the historical flow performance of liquid in the reservoir. The three equal probability random simulation models have been selected optimally. Simulation results show that the stochastic geological model 1 is the best one and its streamlines chart is given. It is easy to see the direction and amount of fluid flowing from injectors to producers with streamlines, and the connectivity between injectors and producers is also identified with high accuracy.

Key concepts: Streamlines, streaklines, and pathlines, Computer science, Stochastic simulation, Convergence (economics), Reservoir simulation, Computer simulation, Reservoir engineering, Gaussian

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