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- W3018248831 abstract "For oil fields that are at a late stage of development, urgent tasks are the operational analysis of the development and optimization of the operating modes of injection wells. The demand for responsiveness often forces one to abandon the use of three-dimensional hydrodynamic models in favor of analytical ones such as CRMP. Using CRMP models allows you to quickly assess the trends in the impact of injection wells on producing wells and build reliable short-term forecasts for fluid production. Supplementing the traditional (single-phase) CRMP model with a water cut model also allows predicting oil production rates for producing wells and expands the capabilities of an operational analysis of the existing development system. In addition, an adequate water cut model allows using the CRMP model to solve the problem of optimizing the operating modes of the injection well stock. This article discusses the main known water cut models used in conjunction with the CRMP model, provides a brief analysis of their advantages and disadvantages. A new authorial mathematical model of water cut (“multi-characteristic model”) is proposed, which allows to establish the role of each injection well in changing the water content of the considered producer. An adaptation algorithm is also described, that is, the selection of unknown model coefficients implemented in Ariadna software (developed by Tyumen Petroleum Research Center LLC). The low computational complexity of the algorithm allows you to quickly simulate areas containing up to several hundred wells. The results of experiments on the use of a new mathematical model on a synthetic model of an oil reservoir are presented. The results of predicting water cut are compared with the results of previously known methods. The restrictions for using the new model, as well as directions for its development are indicated." @default.
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- W3018248831 date "2020-01-01" @default.
- W3018248831 modified "2023-09-27" @default.
- W3018248831 title "A new approach to water cut forecasting based on results of capacitance resistance modeling" @default.
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- W3018248831 doi "https://doi.org/10.21684/2411-7978-2020-6-1-192-207" @default.
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