Аннотация
Summary In recent years, machine learning methods have been widely used in various fields of science for big data processing. The application of machine learning in the oil industry is also actively expanding. To solve oil recovery problems, it is necessary to use geological models of reservoir fields. With increasing of the reservoir model complexity (size), the computing time also increases. Therefore, it takes longer to predict oil recovery. There are two approaches to solve this problem. The first approach is to develop an effective parallel algorithm taking into account the heterogeneity of computing systems. Many scientists from all over the world are developing parallel algorithms in this field. In particular, we have written many scientific papers. The disadvantage of using this approach is that when you change the initial data for the oil recovery prediction, you need to make calculations on supercomputers every time, which takes a lot of time and resources. The second approach is to use machine learning methods, which is the purpose of this paper. This paper discusses approaches to using effective machine learning methods for oil recovery prediction. To train the system, we used historical data from the oil field and synthetic data obtained from surrogate models based on two wells (injection and production). Synthetic data were generated based on mathematical models (oil displacement models, enhanced oil recovery models) by varying the different geological parameters. This problem belongs to the “supervised learning” - type of machine learning. Supervised learning requires a complete set of marked data for training the model at all stages of its construction. When implementing the algorithm, we considered machine learning methods for solving the regression and classification problems. As a result, it was discovered that compared to traditional computational experiments on a regular grid, calculations using machine learning methods are more productive.