The paper discusses the problem of increasing the discreteness of liquid flow rate measurements in a well using highly discrete pressure data at the receiving end of an electric submersible pump (ESP) unit. The «virtual flow meter» algorithm based on machine learning methods that solves the problem is presented. The numerical characteristics describing the curve of pressure change at the ESP unit inlet, as well as the components of Darcy's law and diffusivity equation are considered as features. To solve the regression problem, the authors considered single machine learning models and ensembles based on stacking method of combining the responses of single models as attributes to calculate the responses of the final model. The results of testing on field data on mechanized production wells on examples of low-permeability reservoir of Western Siberia field showed that the average relative error does not exceed 10 %. The algorithm of «virtual flow meter» was implemented in the software package for interpretation of well test «RN-VEGA» and used in preparation of data for interpretation of well-testing by the production and pressure transient analysis. To approbate the approach under consideration, the results of interpretation by the production and pressure transient analysis were compared on data sets with different discreteness of measurements of downhole pressure and liquid rate dynamics. In the first set the liquid rate series had low discreteness, the second set was obtained from the first one by applying the constructed algorithm. It is shown that the use of the «virtual flow meter» reduces the error by 10 % in determining the fracture half-length and permeability of the formation. The results of approbation allow the authors to conclude that the developed algorithm increases the reliability of data interpretation by production and pressure transient analysis, as well as increases the accuracy of determining reservoir parameters and well completion in low-permeability reservoirs.
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