Diagnostic of Operation Conditions and Sensor Faults Using Machine Learning in Sucker-Rod Pumping Wells

Abstract

In sucker-rod pumping wells, due to the lack of an early diagnosis of operating condition or sensor faults, several problems can go unnoticed. These problems can increase downtime and production loss. In these wells, the diagnosis of operation conditions is carried out through downhole dynamometer cards, via pre-established patterns, with human visual effort in the operation centers. Starting with machine learning algorithms, several papers have been published on the subject, but it is still common to have doubts concerning the difficulty level of the dynamometer card classification task and best practices for solving the problem. In the search for answers to these questions, this work carried out sixty tests with more than 50,000 dynamometer cards from 38 wells in the Mossoró, RN, Brazil. In addition, it presented test results for three algorithms (decision tree, random forest and XGBoost), three descriptors (Fourier, wavelet and card load values), as well as pipelines provided by automated machine learning. Tests with and without the tuning of hypermeters, different levels of dataset balancing and various evaluation metrics were evaluated. The research shows that it is possible to detect sensor failures from dynamometer cards. Of the results that will be presented, 75% of the tests had an accuracy above 92% and the maximum accuracy was 99.84%.

Diagnosis of Operating Conditions of the Electrical Submersible Pump via Machine Learning

“…The last step of this classification process (model validation) was to analyze whether the results obtained were consistent from the validation of metrics. The steps mentioned are shown in Figure 3 [ 28 ].…”

“… Common steps for trying out a machine learning project. These steps were used in the development of this work (adapted from [ 28 ]). …”

Unveiling Deep Learning Insights: A Specialized Analysis of Sucker Rod Pump Dynamographs, Emphasizing Visualizations and Human Insight

“…Moreover, the traditional interpretation of dyna cards may not detect early-stage anomalies, which can result in delayed action and increased operational risks [34]. For example, subtle changes that may be indicative of a developing problem (such as a slowly wearing pump or gradually worsening gas interference) might not be noticeable until the issue becomes more pronounced.…”