Abstract
The paper proposes an algorithm for determining the flow rate of an oil producing well based on dynamometer data. The dynamogram is presented as a two-dimensional image processed using a convolutional neural network that solves the problem of nonlinear regression between the dynamogram image and the flow rate value. The structure of the dynamometer data collection and processing system is presented, the main steps of the data analysis algorithm are described. The resulting model makes it possible to estimate the production rate with an error in the range of 15-20%.
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