Water Flooding Performance Prediction in Layered Reservoir Using Big Data and Artificial Intelligence Algorithms

Abstract

Managing oil production from reservoirs to maximize the future economic return of the asset is an important issue in petroleum engineering. One of the most important problems is the prediction of water flooding performance. Traditional strategies have been widely used with a long run time and too much information to solve this problem. Therefore, it is urgent to form a fast intelligent prediction method, especially with the development of large data processing and artificial intelligence methods. This paper proposed a new method to predict water flooding performance using big data and artificial intelligence algorithms. The method regards layered reservoir as a vertical superposition of a series of single layer reservoirs. An injection-production analysis model is established in each single layer reservoir respectively. And then a superposition model is established only by production data and logging tools data. Finally, the least square principle and the particle swarm optimization algorithm are used to optimize the model and predict water flooding performance. This method has been tested for different synthetic reservoir case studies. The results are in good agreement in comparison with the numerical simulation results. The average relative error is 4.59%, but the calculation time is only 1/10 of that of numerical simulation by using artificial intelligence method. It showed that this technique has capability to predict water flooding performance. These examples showed that the use of artificial intelligence method not only greatly shortens the working time, but also has a higher accuracy. By this paper, it is possible to predict the water flooding performance easily and accurately in reservoirs. It has an important role in the field development, increasing or decreasing investment, drilling new wells and future injection schedule.