Pattern Recognition  for Downhole Dynamometer Card in Oil Rod Pump System Using Artificial Neural Networks

Bezerra, Marco A. D.; Schnitman, Leizer; Filho, M. de A. Barreto; Souza, J. A. M. Felippe de

doi:10.5220/0002000403510355

Cited by 27 publications

(13 citation statements)

References 4 publications

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“…However, the studies conducted using deep learning have mostly used neural networks trained from scratch. Some of the work that was performed required over 1,440 epochs/iterations for the neural network to converge (Bezerra 2009).…”

Section: Machine Learning With Manual Feature Extractionmentioning

confidence: 99%

Beam Pump Dynamometer Card Prediction using Artificial Neural Networks

Sharaf¹

2018

KEG

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Section: Machine Learning With Manual Feature Extractionmentioning

confidence: 99%

Beam Pump Dynamometer Card Prediction using Artificial Neural Networks

Sharaf¹

2018

KEG

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“…Indicator diagrams are viewed as one of the important basic sources in the fault diagnosis of pumping wells and the evaluation of oil production [1][2][3]. erefore, the intelligent identification and analyses of indicator diagrams are quite significant for petroleum engineering and production management [4,5].…”

Section: Introductionmentioning

confidence: 99%

Fault Recognition of Indicator Diagrams Based on the Dynamic Time Warping Distance of Differential Curves

Zhao

Zhang

2021

Mathematical Problems in Engineering

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Oil recovery with pumping units is widely used in oilfield development at present. The management level of pumping units is related to the overall economic benefit of oilfield. In order to effectively control the production management of pumping wells, it is necessary to take all the production data to formulate a reasonable working system and make continuous analyses about the daily management level of pumping wells. The analysis and interpretation of the indicator diagram is a direct understanding way about the situation of deep well pumps in petroleum engineering. However, the complicated practical oilfield conditions often lead to the distortion of regular indicator diagrams, resulting in the low efficiency of fault-type diagnosis and wrong recognition. A new method of identifying the fault type of indicator diagrams is proposed by using the dynamic time warping (DTW) distance, through which a two-dimensional indicator diagram is transformed into a one-dimensional differential curve. The experimental results of real indicator diagrams show that the method is effective for the type diagnosis of indicator diagrams with high accuracy and fast speed.

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“…Many advanced analytical methods were used in classification of Dynamometer Cards in several works, such as hierarchical systems specialists (Abello, Houang, and Russell 1993), symbolic neural networks (Corrêa 1995), artificial neural networks (Bezerra, Schnitman, and Filho 2009), analysis of frequency spectrum (de Lima, Guedes, and Silva 2009) and Support Vector Machines (Li et al 2013).…”

Section: Introductionmentioning

confidence: 99%

A New Approach To Diagnosis of Sucker Rod Pump Systems by Analyzing Segments of Downhole Dynamometer Cards

et al. 2015

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This work proposes a new method of diagnosing the Sucker Rod Pump Systems, by analyzing segments between the points of opening and closing valves identified in the Downhole Dynamometer Cards. An analysis of the shape of the pumping condition classes represented in the Downhole Dynamometer Cards, especially regarding the behavior of segments between the valve opening and closing points is presented. The feasibility of the classification of Downhole Dynamometer Cards by the curvature characteristics of segments is demonstrated. Then, methods of classification of 16 pumping condition classes are presented, 4 by simple statistics and 12 of them extracting curvature characteristics of the segments andusing statistics to implement Mamdani fuzzy inference systems. Tests are developed with classifiers created by the method, and the test results over a library of Dynamometer Cards from real wells, previously classified by human experts, demonstrate the feasibility of the approach and the precision of the classification even without a deeper study to choose the better characteristics and statistics. In addition the classifiers were developed using samples of Dynamometer Cards from the literature, showing that the method reduces the need of many samples of the great variety of the Dynamometer Cards Shapes from real wells. Different from other methods, these results indicate the severity degree of many pumping condition classes and demonstrate the concomitant pumping condition detection feature. The classifiers were even able to spot misleading classifications by the human experts thanks to deeper understanding of the behavior of the Dynamometer Cards segments in the case of one or more anomalies in the system. The features to quantify and identify concomitant classes of so many pumping conditionsdemonstrated in this approach are novel in the area. The paper shows the feasibility of the approach and the potential for greater identification precision.

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Pattern Recognition for Downhole Dynamometer Card in Oil Rod Pump System Using Artificial Neural Networks

Cited by 27 publications

References 4 publications

Beam Pump Dynamometer Card Prediction using Artificial Neural Networks

Beam Pump Dynamometer Card Prediction using Artificial Neural Networks

Fault Recognition of Indicator Diagrams Based on the Dynamic Time Warping Distance of Differential Curves

A New Approach To Diagnosis of Sucker Rod Pump Systems by Analyzing Segments of Downhole Dynamometer Cards

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