Diagnóstico das condições de operação e falhas de sensor em poços operando por bombeio mecânico utilizando Machine Learning
In oil fields with many wells operated by sucker-rod pumping, due to the lack of early diagnosis of operational conditions or sensor failures, several problems can go unnoticed. These problems, can lead to significant losses, such as increased downtime, higher operational expenses (OPEX), decrease...
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| Formato: | doctoralThesis |
| Idioma: | pt_BR |
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Universidade Federal do Rio Grande do Norte
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| Endereço do item: | https://repositorio.ufrn.br/handle/123456789/54867 |
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| Resumo: | In oil fields with many wells operated by sucker-rod pumping, due to the lack of early
diagnosis of operational conditions or sensor failures, several problems can go unnoticed. These problems, can lead to significant losses, such as increased downtime, higher
operational expenses (OPEX), decreased efficiency and production loss. In practice, the
identification and diagnosis of operational conditions are primarily performed based on
surface and downhole dynamometer cards, using pre-established patterns and relying on
human visual inspection at operation centers. This task consumes a lot of time and effort
and requires expertise, as it can be influenced by subjective factors. However, in recent
years, with the advancements in Machine Learning (ML) algorithms, several research
studies in this field have achieved promising results in diagnosing operational conditions,
demonstrating the potential of ML for this purpose. Nevertheless, there are still common
doubts regarding the difficulty level of classifying dynamometer cards, the best algorithm,
the most suitable shape descriptor, the optimal metrics, and the impact of imbalanced datasets. In pursuit of answers to these questions, this work utilized real data from 38 wells
operating via sucker-rod pumping in the Mossoró region, RN, Brazil. Over 50,000 cards
were classified by the author of the work and categorized into eight operating modes and
two common sensor failures in this field. Seventy-three tests were conducted and divided
into nine groups. Three algorithms, namely Decision Tree, Random Forest, and XGBoost,
were tested with and without hyperparameters tuning, along with pipelines provided by
Automated Machine Learning (AutoML). The descriptors used included Centroid, Fourier, and Wavelet, in addition to the load values from the dynamometer card. Both balanced and imbalanced datasets were tested. The results confirm the feasibility of applying
ML for diagnosing operational conditions and sensor failures in sucker-rod pumping systems, as 80.82% of the tests achieved an accuracy above 90%, with a remarkable accuracy
of 99.9% also being attained. Finally, a portion of the implementations suggested in this
research was evaluated in a production environment. Dynamometer cards from over a
hundred wells were assessed, yielding an accuracy exceeding 96%, which reinforces the
effectiveness of the implementations in a real-world context. Overall, the results highlight
the potential of ML as a promising approach for diagnosing operational conditions and
sensor failures in sucker-rod pumping systems. |
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