Beam Pump Dynamometer Card Prediction using Artificial Neural Networks

Sharaf, Sayed

doi:10.18502/keg.v3i7.3083

Cited by 11 publications

(4 citation statements)

References 4 publications

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“…The results demonstrated that CNN-based approach is superior to the conventional approaches without any need of manual feature extraction that requires domain expertise. In [ 21 ], the potential of using artificial neural networks in well fault diagnosis was reviewed and analyzed. VGG16, ResNet34, and ResNeXt50 were used to recognize beam pump conditions based on a pump card shape.…”

Section: Related Workmentioning

confidence: 99%

“…The accuracy and efficiency have greatly been improved by these approaches. However, the conducted studies using deep learning have mostly relied on neural networks trained from scratch, which generally requires numerous epochs or iterations for a deep neural network to converge [ 21 ]. Some methods need an amount of time to train the model and classify the pattern.…”

Section: Related Workmentioning

confidence: 99%

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Automatic Recognition of Sucker-Rod Pumping System Working Conditions Using Dynamometer Cards with Transfer Learning and SVM

Cheng

Huang

Zeng

et al. 2020

Sensors

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Sucker-rod pumping systems are the most widely applied artificial lift equipment in the oil and gas industry. Accurate and intelligent working condition recognition of pumping systems imposes major impacts on oilfield production benefits and efficiency. The shape of dynamometer card reflects the working conditions of sucker-rod pumping systems, and different conditions can be indicated by their typical card characteristics. In traditional identification methods, however, features are manually extracted based on specialist experience and domain knowledge. In this paper, an automatic fault diagnosis method is proposed to recognize the working conditions of sucker-rod pumping systems with massive dynamometer card data collected by sensors. Firstly, AlexNet-based transfer learning is adopted to automatically extract representative features from various dynamometer cards. Secondly, with the extracted features, error-correcting output codes model-based SVM is designed to identify the working conditions and improve the fault diagnosis accuracy and efficiency. The proposed AlexNet-SVM algorithm is validated against a real dataset from an oilfield. The results reveal that the proposed method reduces the need for human labor and improves the recognition accuracy.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Automatic Recognition of Sucker-Rod Pumping System Working Conditions Using Dynamometer Cards with Transfer Learning and SVM

Cheng

Huang

Zeng

et al. 2020

Sensors

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“…Pre-trained deep neural networks with the source data were used in [13][14][15][16][17][18][19][20][21][22] by frozing its partial parameters, and then part of network parameters were transferred to the target network and other parameters were fine-tuned with a small amount of target data. Pre-trained deep neural networks on ImageNet were used in [23][24][25][26][27][28] and were fine-tuned with limited target data to adapt the domain of engineer applications. Qureshi et al [29] pre-trained nine deep sparse auto-encoders on a wind farm, and predictions on other wind farm datasets were taken by fine-tuning the pre-trained networks.…”

Section: Brief Reviewmentioning

confidence: 99%

Applications of Unsupervised Deep Transfer Learning to Intelligent Fault Diagnosis: A Survey and Comparative Study

Zhao,

Zhang,

et al. 2019

Preprint

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Recent progress on intelligent fault diagnosis has greatly depended on the deep learning and plenty of labeled data. However, the machine often operates with various working conditions or the target task has different distributions with the collected data used for training (we called the domain shift problem). This leads to the deep transfer learning based (DTL-based) intelligent fault diagnosis which attempts to remit this domain shift problem. Besides, the newly collected testing data are usually unlabeled, which results in the subclass DTL-based methods called unsupervised deep transfer learning based (UDTL-based) intelligent fault diagnosis. Although it has achieved huge development in the field of fault diagnosis, a standard and open source code framework and a comparative study for UDTL-based intelligent fault diagnosis are not yet established. In this paper, commonly used UDTL-based algorithms in intelligent fault diagnosis are integrated into a unified testing framework and the framework is tested on five datasets. Extensive experiments are performed to provide a systematically comparative analysis and the benchmark accuracy for more comparable and meaningful further studies. To emphasize the importance and reproducibility of UDTL-based intelligent fault diagnosis, the testing framework with source codes will be released to the research community to facilitate future research. Finally, comparative analysis of results also reveals some open and essential issues in DTL for intelligent fault diagnosis which are rarely studied including transferability of features, influence of backbones, negative transfer, and physical priors. In summary, the released framework and comparative study can serve as an extended interface and the benchmark results to carry out new studies on UDTL-based intelligent fault diagnosis. The code framework is available at https://github. com/ZhaoZhibin/UDTL.

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“…e existing digital resources are helpful to automatically identify the shape and characteristics of indicator diagrams, which have become a hot spot in the research of oil development [9,10]. Currently, some methods, e.g., the back-propagation (BP) neural networks [11,12], the radial basis function (RBF) [13], the extreme learning machine [14], and the convolution neural networks (CNNs) [15], have been applied to the fault diagnosis of pumping units and are gradually replacing the traditional artificial analysis.…”

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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Beam Pump Dynamometer Card Prediction using Artificial Neural Networks

Abstract: .

Cited by 11 publications

References 4 publications

Automatic Recognition of Sucker-Rod Pumping System Working Conditions Using Dynamometer Cards with Transfer Learning and SVM

Automatic Recognition of Sucker-Rod Pumping System Working Conditions Using Dynamometer Cards with Transfer Learning and SVM

Applications of Unsupervised Deep Transfer Learning to Intelligent Fault Diagnosis: A Survey and Comparative Study

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

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