scholarly journals Research on Feature Extraction of Indicator Card Data for Sucker-Rod Pump Working Condition Diagnosis

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Yunhua Yu ◽  
Haitao Shi ◽  
Lifei Mi
Keyword(s):  
Feature Extraction ◽  
Working Condition ◽  
Fourier Descriptors ◽  
Gray Level ◽  
Memory Space ◽  
Geometric Moment ◽  
Moment Vector ◽  
Sucker Rod ◽  
Sucker Rod Pump ◽  
Condition Diagnosis

Three feature extraction methods of sucker-rod pump indicator card data have been studied, simulated, and compared in this paper, which are based on Fourier Descriptors (FD), Geometric Moment Vector (GMV), and Gray Level Matrix Statistics (GLMX), respectively. Numerical experiments show that the Fourier Descriptors algorithm requires less running time and less memory space with possible loss of information due to nonoptimal numbers of Fourier Descriptors, the Geometric Moment Vector algorithm is more time-consuming and requires more memory space, while the Gray Level Matrix Statistics algorithm provides low-dimension feature vectors with more time consumption and more memory space. Furthermore, the characteristic of rotational invariance, both in the Fourier Descriptors algorithm and the Geometric Moment Vector algorithm, may result in improper pattern recognition of indicator card data when used for sucker-rod pump working condition diagnosis.

A Working Condition Diagnosis Model of Sucker Rod Pumping Wells Based on Big Data Deep Learning

2019 ◽  
Author(s):  
Xiang Wang ◽  
Yanfeng He ◽  
Fajun Li ◽  
Xiangji Dou ◽  
Zhen Wang ◽  
...  
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Working Condition ◽  
Pumping Wells ◽  
Sucker Rod ◽  
Diagnosis Model ◽  
Condition Diagnosis

A Working Condition Diagnosis Model of Sucker Rod Pumping Wells Based on Deep Learning

2021 ◽  
pp. 1-10
Author(s):  
Xiang Wang ◽  
Yangeng He ◽  
Fajun Li ◽  
Zhen Wang ◽  
Xiangji Dou ◽  
...  
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Working Conditions ◽  
Working Condition ◽  
Data Set ◽  
Pumping Wells ◽  
Sucker Rod ◽  
Diagnosis Model ◽  
Dynamometer Card ◽  
Condition Diagnosis

Summary Monitoring the working conditions of sucker rod pumping wells in a timely and accurate manner is important for oil production. With the development of smart oil fields, more and more sensors are installed on the well, and the monitored data are continuously transmitted to the data center to form big data. In this work, we aim to utilize the big data collected during oil well production and a deep learning technique to build a new generation of intelligent diagnosis model to monitor working condition of sucker rod pumping wells. More than 5×106 of well monitoring records, which covers information from about 1 year for more than 300 wells in an oilfield block, are collected and preprocessed. To show the dynamic changes of the working conditions for the wells, the overlay dynamometer card is proposed and plotted for each data record. The working conditions are divided into 30 types, and the corresponding data set is created. An intelligent diagnosis model using the convolutional neural network (CNN), one of the deep learning frameworks, is proposed. By the convolution and pooling operation, the CNN can extract features of an image implicitly without human effort and prior knowledge. That makes a CNN very suitable for the recognition of the overlay dynamometer cards. The architecture for a working condition diagnosis CNN model is designed. The CNN model consists of 14 layers with six convolutional layers, three pooling layers, and three fully connected layers. The total number of neurons is more than 1.7×106. The overlay dynamometer card data set is used to train and validate the CNN model. The accuracy and efficiency of the model are evaluated. Both the training and validation accuracies of the CNN model are greater than 99% after 10 training epochs. The average training elapsed time for an epoch is 8909.5 seconds, and the average time to diagnosis a sample is 1.3 milliseconds. Based on the trained CNN model, a working condition monitoring software for a sucker rod pumping well is developed. The software runs 7 × 24 hours to diagnosis the working conditions of wells and post a warning to users. It also has a feedback learning workflow to update the CNN model regularly to improve its performance. The on-site run shows that the actual accuracy of the CNN model is greater than 90%.

Modeling of Sucker rod pump in CBM wells using QRod Simulator

2011 ◽  
Vol 3 (5) ◽  
pp. 274-276
Author(s):  
Siraj Bhatkar ◽  
◽  
Yusufuddin Nehri ◽  
Fahad Shaikh
Keyword(s):  
Sucker Rod ◽  
Sucker Rod Pump

Gray-Level Co-occurrence Matrix and Random Forest Based Off-line Odia Handwritten Character Recognition

2019 ◽  
Vol 13 (2) ◽  
pp. 136-141 ◽  
Author(s):  
Abhisek Sethy ◽  
Prashanta Kumar Patra ◽  
Deepak Ranjan Nayak
Keyword(s):  
Feature Extraction ◽  
Random Forest ◽  
Character Recognition ◽  
Recognition Rate ◽  
Discrete Wavelet ◽  
Gray Level ◽  
Handwritten Character Recognition ◽  
Handwritten Character ◽  
Wide Range ◽  
Occurrence Matrix

Background: In the past decades, handwritten character recognition has received considerable attention from researchers across the globe because of its wide range of applications in daily life. From the literature, it has been observed that there is limited study on various handwritten Indian scripts and Odia is one of them. We revised some of the patents relating to handwritten character recognition. Methods: This paper deals with the development of an automatic recognition system for offline handwritten Odia character recognition. In this case, prior to feature extraction from images, preprocessing has been done on the character images. For feature extraction, first the gray level co-occurrence matrix (GLCM) is computed from all the sub-bands of two-dimensional discrete wavelet transform (2D DWT) and thereafter, feature descriptors such as energy, entropy, correlation, homogeneity, and contrast are calculated from GLCMs which are termed as the primary feature vector. In order to further reduce the feature space and generate more relevant features, principal component analysis (PCA) has been employed. Because of the several salient features of random forest (RF) and K- nearest neighbor (K-NN), they have become a significant choice in pattern classification tasks and therefore, both RF and K-NN are separately applied in this study for segregation of character images. Results: All the experiments were performed on a system having specification as windows 8, 64-bit operating system, and Intel (R) i7 – 4770 CPU @ 3.40 GHz. Simulations were conducted through Matlab2014a on a standard database named as NIT Rourkela Odia Database. Conclusion: The proposed system has been validated on a standard database. The simulation results based on 10-fold cross-validation scenario demonstrate that the proposed system earns better accuracy than the existing methods while requiring least number of features. The recognition rate using RF and K-NN classifier is found to be 94.6% and 96.4% respectively.

scholarly journals A comprehensive review of sucker rod pumps’ components, diagnostics, mathematical models, and common failures and mitigations

2021 ◽  
Author(s):  
Sherif Fakher ◽  
Abdelaziz Khlaifat ◽  
M. Enamul Hossain ◽  
Hashim Nameer
Keyword(s):  
Mathematical Models ◽  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Gas Industry ◽  
Holistic View ◽  
Comprehensive Review ◽  
Artificial Lift ◽  
Sucker Rod ◽  
Sucker Rod Pump

AbstractIn many oil reservoirs worldwide, the downhole pressure does not have the ability to lift the produced fluids to the surface. In order to produce these fluids, pumps are used to artificially lift the fluids; this method is referred to as artificial lift. More than seventy percent of all currently producing oil wells are being produced by artificial lift methods. One of the most applied artificial lift methods is sucker rod pump. Sucker rod pumps are considered a well-established technology in the oil and gas industry and thus are easy to apply, very common worldwide, and low in capital and operational costs. Many advancements in technology have been applied to improve sucker rod pumps performance, applicability range, and diagnostics. With these advancements, it is important to be able to constantly provide an updated review and guide to the utilization of the sucker rod pumps. This research provides an updated comprehensive review of sucker rod pumps components, diagnostics methods, mathematical models, and common failures experienced in the field and how to prevent and mitigate these failures. Based on the review conducted, a new classification of all the methods that can fall under the sucker rod pump technology based on newly introduced sucker rod pump methods in the industry has been introduced. Several field cases studies from wells worldwide are also discussed in this research to highlight some of the main features of sucker rod pumps. Finally, the advantages and limitations of sucker rod pumps are mentioned based on the updated review. The findings of this study can help increase the understanding of the different sucker rod pumps and provide a holistic view of the beam rod pump and its properties and modeling.

scholarly journals Modeling the Process of Event Sequence Data Generated for Working Condition Diagnosis

2015 ◽  
Vol 2015 ◽  
pp. 1-13
Author(s):  
Jianwei Ding ◽  
Yingbo Liu ◽  
Li Zhang ◽  
Jianmin Wang
Keyword(s):  
Working Condition ◽  
Sequence Data ◽  
A Priori ◽  
Real Data ◽  
Data Sets ◽  
Main Task ◽  
Event Sequence ◽  
Telemetry Data ◽  
Condition Monitoring Systems ◽  
Condition Diagnosis

Condition monitoring systems are widely used to monitor the working condition of equipment, generating a vast amount and variety of telemetry data in the process. The main task of surveillance focuses on analyzing these routinely collected telemetry data to help analyze the working condition in the equipment. However, with the rapid increase in the volume of telemetry data, it is a nontrivial task to analyze all the telemetry data to understand the working condition of the equipment without any a priori knowledge. In this paper, we proposed a probabilistic generative model called working condition model (WCM), which is capable of simulating the process of event sequence data generated and depicting the working condition of equipment at runtime. With the help of WCM, we are able to analyze how the event sequence data behave in different working modes and meanwhile to detect the working mode of an event sequence (working condition diagnosis). Furthermore, we have applied WCM to illustrative applications like automated detection of an anomalous event sequence for the runtime of equipment. Our experimental results on the real data sets demonstrate the effectiveness of the model.

Sign in / Sign up

Export Citation Format

Share Document