Diagnóstico de condições de operação do bombeio centrífugo submerso utilizando machine learning
In artificial lift, Automation techniques are also used in order to increase the efficiency and production of oil wells. In the Electrical Submersible Pump (ESP) lift method, the use of Automation tools becomes essential in the interpretation of data available in the field, since the analysis of...
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| 格式: | Dissertação |
| 語言: | pt_BR |
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Universidade Federal do Rio Grande do Norte
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| 在線閱讀: | https://repositorio.ufrn.br/handle/123456789/49240 |
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| 總結: | In artificial lift, Automation techniques are also used in order to increase the efficiency and
production of oil wells. In the Electrical Submersible Pump (ESP) lift method, the use of
Automation tools becomes essential in the interpretation of data available in the field, since the
analysis of these data is not always sufficient to analyze, interpret, monitor and diagnose the
performance, and well integrity, in addition to ESP operation and real-time efficiency. However,
even though these wells operate with automated systems, some damages in production can be
identified, reducing the efficiency of the ESP pump and significant losses in the flows obtained
may occur. Initial diagnosis of the ESP system can lead to great cost savings and less maintenance
due to technologies implemented in production fields. In oil fields, to identify the operating
conditions of a ESP well, amperimetric charts are used, which are graphs of current versus time.
The analysis of these charts is usually performed by operators who have a large number of wells
to examine, and this overload often decreases the efficiency in the process of reading the operating
conditions of the ESP pump. Currently, real-time technologies based on Machine Learning (ML)
algorithms have challenged and encouraged companies to create solutions for early diagnosis of
abnormalities in well operation. Thus, this work aims to provide a proposal for detecting the
operating conditions (normal operation, normal operation with gas, gas interference and gas
locking) of the ESP pump from the analysis of electrical current data obtained from 24 wells from
Mossoró, RN, Brazil. Machine Learning classification algorithms were implemented in the Python
programming language in the Google Colaboratory® environment. The classification algorithms
used were Decision Tree (DT), Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and
Multi-Layer Perceptron Neural Network (MLP). As the data sets had points ranging from 159 to
344, a standardization was performed with an interpolation technique so that all data sets had 344
points, the maximum number of points collected. The algorithms were tested with and without
hyperparameter tuning, considering that for each ML technique there was a specific set of
hyperparameters. In addition, balancing tests (oversampling) of the training datasets were
performed to identify the difference in relation to the unbalanced dataset. The results obtained and
presented throughout the work confirm that the application of the ML algorithm is viable for the
classification of the operating conditions presented, since all of them had an accuracy greater than
87%, with the best result being the application of the SVM model, which reached an accuracy of 93%. |
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