Automated system for interpreting technical condition from dynamograms based on machine learning tools

UDK: 681.518:622.276.58
DOI: 10.24887/0028-2448-2021-4-102-105
Key words: oil production, sucker rod pumps, dynamogram, machine learning, diagnostics
Authors: M.G. Volkov (RN-BashNIPIneft LLC, RF, Ufa), D.V. Silnov (RN-BashNIPIneft LLC, RF, Ufa), A.S. Topolnikov (RN-BashNIPIneft LLC, RF, Ufa), B.M. Latypov (RN-BashNIPIneft LLC, RF, Ufa), A.V. Katermin (Bashneft PJSOC, RF, Ufa), R.M. Enikeev (Bashneft PJSOC, RF, Ufa)
The article presents the results of work on the development of an automated system for interpreting deviations from dynamograms based on machine learning tools. The work contains the results of factor analysis of the reasons affecting the accuracy of the dynamometer recording of the sucker rod pump and the reasons affecting the accuracy of the dynamogram interpretation models and the principle of the implementation of the tool for recognizing deviations in work dynamometer sucker rod pump. It has been shown that the accuracy of a dynamogram is influenced by many factors, such as: the state of the polished rod (dimensions) due to deviations caused by abrasion and wear, the deviation of the elastic modulus of the steel grade of the polished rod from the calculated value, the deviation of the Poisson coefficient, and the error from temperature drift by the device itself. It is shown that the quality of the implemented machine learning model will be affected by: the quality of the training sample and the test sample (the number of erroneous interpretations in the samples); prognostic ability of the model itself. The scheme of operation of the system for interpreting deviations from dynamograms and the results of assessing the quality of the developed models are presented. For the model of binary classification of dynamograms, the Fisher metric was 97%, for the multiclass model - 82%, for the multilable model - 87%. The developed automated system for interpreting deviations from dynamograms based on machine learning tools is integrated into the decision support system implemented as part of R&D project “Operational Service” Bashneft PJSOC. The system allows you to quickly identify simultaneously several types of deviations in the dynamogram.
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