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- W4323784339 abstract "At any field, there is a drop in well production over time. The reason for this can be many factors, ranging from technical parameters (the distance between the clusters of wells, the distance between the faces of wells, etc.) and ending with geological or physico-chemical characteristics. During modeling, the main indicator reflecting the dynamics of the well operation is proposed to use the productivity coefficient. By changing this coefficient, it is possible to judge the instability of the well or reservoir operation. Many factors can affect the change in the well productivity coefficient, for example, the depression created on the reservoir, the liquid volume coefficient, the liquid viscosity, as well as the oil phase permeability. The combination of all complications leads to a sharp decrease in the process indicators of equipment and installations in whole. In this regard, there is a question of increasing the efficiency of the oil field equipment operating modes based on well productivity coefficients. The analysis indicated that in automating the process of developing recommendations for choosing of a method for improving the equipment operating modes efficiency based on well productivity coefficients, the most effective to use of machine learning." @default.
- W4323784339 created "2023-03-11" @default.
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- W4323784339 date "2023-01-01" @default.
- W4323784339 modified "2023-09-25" @default.
- W4323784339 title "Prediction of the productivity coefficient of wells using machine learning in the Matlab program" @default.
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- W4323784339 doi "https://doi.org/10.1063/5.0137263" @default.
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