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

Bojan Martinović (), Milos Bijanić, Dusan Danilović, Andrija Petrović and Boris Delibasić
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Bojan Martinović: Scientific and Technology Center NIS—Naftagas, 21000 Novi Sad, Serbia
Milos Bijanić: Faculty of Organizational Science, University of Belgrade, 11000 Belgrade, Serbia
Dusan Danilović: Faculty of Mining and Geology, University of Belgrade, 11000 Belgrade, Serbia
Andrija Petrović: Faculty of Organizational Science, University of Belgrade, 11000 Belgrade, Serbia
Boris Delibasić: Faculty of Organizational Science, University of Belgrade, 11000 Belgrade, Serbia

Mathematics, 2023, vol. 11, issue 23, 1-29

Abstract: This study delves into the heightened efficiency and accuracy of 11 deep learning models classifying 11 dynamograph classes in the oil production sector. Introducing a novel framework with the Grad–CAM method, we address the “black box” issue, providing transparency in the models’ decision-making processes. Our analysis includes a comparative study with human experts, revealing a comprehensive understanding of both machine and human interpretive strategies. Results highlight the notable speed and precision of machine learning models, marking a significant advancement in rapid, reliable dynamograph classification for oil production decision-making. Additionally, nuanced findings in the model’s diagnostic accuracy reveal limitations in situations featuring the simultaneous occurrence of multiple pump issues. This underscores the need for additional features and domain-specific logic to enhance discernment and diagnostic precision in complex scenarios. The exploration of qualitative aspects distinguishes interpretive approaches, highlighting strengths and limitations. Machines, driven by algorithmic patterns and data processing, excel in rapid identification, albeit with occasional misclassifications. In contrast, human experts leverage experience and domain-specific knowledge for nuanced interpretation, providing a comprehensive understanding of both quantitative metrics and qualitative nuances. In conclusion, this study not only demonstrates the accelerated and enhanced accuracy of dynamograph classification by machine learning models compared to junior and medior domain experts, but also provides valuable insights into specific features and patterns guiding the decision-making process. This understanding allows continuous refinement, combining machine speed with human understanding for improved results in oil production. The potential for further studies and improvements in this domain is substantial.

Keywords: artificial intelligence; sucker-rod pump; dynamograph; deep learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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