The Experts below are selected from a list of 321 Experts worldwide ranked by ideXlab platform
Peng Zhongxiao - One of the best experts on this subject based on the ideXlab platform.
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Implementation of Network-computing and NN based Remote Real-time Oil Well Monitoring System
2005 International Conference on Neural Networks and Brain, 2005Co-Authors: Li Hongsheng, Y. Ding, Wang Yu, Peng ZhongxiaoAbstract:This paper introduces a real-time Oil Well monitoring system based on network computing and neural network (NN) technologies. In the system, some enterprise computing techniques such as browser/server Web mode, JMS, message-oriented middleware (MOM) and Java Applet are employed. In addition, GPRS wireless communication is used to achieve remote transmission of Oil Well data. This scheme makes adopts Java Applet that operates at client side (Web browser) to receive messages "pushed" by server through JMS (Java Message Service). In this way, server and client are able to communicate in time so that client side can reflect the real-time Oil Well data and the fault diagnosis result. In remote monitoring center, the fault diagnosis station takes the responsibility for the fault detection and diagnosis of Oil pumping units by means of neural networks and evolutionary computation. This solution accomplishes network share of Oil Well information, improves the efficiency of system development. The system has already been applied successfully to an Oil field, and has got the anticipated results
Li Hongsheng - One of the best experts on this subject based on the ideXlab platform.
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Implementation of Network-computing and NN based Remote Real-time Oil Well Monitoring System
2005 International Conference on Neural Networks and Brain, 2005Co-Authors: Li Hongsheng, Y. Ding, Wang Yu, Peng ZhongxiaoAbstract:This paper introduces a real-time Oil Well monitoring system based on network computing and neural network (NN) technologies. In the system, some enterprise computing techniques such as browser/server Web mode, JMS, message-oriented middleware (MOM) and Java Applet are employed. In addition, GPRS wireless communication is used to achieve remote transmission of Oil Well data. This scheme makes adopts Java Applet that operates at client side (Web browser) to receive messages "pushed" by server through JMS (Java Message Service). In this way, server and client are able to communicate in time so that client side can reflect the real-time Oil Well data and the fault diagnosis result. In remote monitoring center, the fault diagnosis station takes the responsibility for the fault detection and diagnosis of Oil pumping units by means of neural networks and evolutionary computation. This solution accomplishes network share of Oil Well information, improves the efficiency of system development. The system has already been applied successfully to an Oil field, and has got the anticipated results
J. R. Rogers - One of the best experts on this subject based on the ideXlab platform.
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Photograph of Oil Well Fire
2020Co-Authors: J. R. RogersAbstract:Photograph of an Oil Well fire in East Texas, with black and white smoke billowing over trees in the distance.
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Photograph of Oil Well
2014Co-Authors: J. R. RogersAbstract:Photograph of an Oil derrick and Oil field. Notes on the photo say "18000 BBLS, 44 Gravity Oil, 1450 Ft., Slick's Eakin No. 4, Pioneer, Tex." and "most remarkable Oil Well on earth."
O. Kaynak - One of the best experts on this subject based on the ideXlab platform.
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Oil Well diagnosis by sensing terminal characteristics of the induction motor
IEEE Transactions on Industrial Electronics, 2000Co-Authors: B.m. Wilamowski, O. KaynakAbstract:Oil Well diagnosis usually requires dedicated sensors placed on the surface and the bottom of the Well. There is significant interest in identifying the characteristics of an Oil Well by using data from these sensors and neural networks for data processing. The purpose of this paper is to identify Oil Well parameters by measuring the terminal characteristics of the induction motor driving the pumpjack. Information about Oil Well properties is hidden in instantaneous power waveforms. The extraction of this information was done using neural networks. For the purpose of training neural networks, a complex model of the system, which included 25 differential equations, was developed. Successful application of neural networks was possible due to the proposed signal preprocessing which reduces thousands of measured data points into 20 scalar variables. The special input pattern transformation was used to enhance the power of the neural networks. Two training algorithms, originally developed by authors, were used in the learning process. The presented approach does not require special instrumentation and can be used on any Oil Well with a pump driven by an induction motor. The quality of the Oil Well could be monitored continuously and proper adjustments could be made. The approach may lead to significant savings in electrical energy, which is required to pump the Oil.
Y. Ding - One of the best experts on this subject based on the ideXlab platform.
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Implementation of Network-computing and NN based Remote Real-time Oil Well Monitoring System
2005 International Conference on Neural Networks and Brain, 2005Co-Authors: Li Hongsheng, Y. Ding, Wang Yu, Peng ZhongxiaoAbstract:This paper introduces a real-time Oil Well monitoring system based on network computing and neural network (NN) technologies. In the system, some enterprise computing techniques such as browser/server Web mode, JMS, message-oriented middleware (MOM) and Java Applet are employed. In addition, GPRS wireless communication is used to achieve remote transmission of Oil Well data. This scheme makes adopts Java Applet that operates at client side (Web browser) to receive messages "pushed" by server through JMS (Java Message Service). In this way, server and client are able to communicate in time so that client side can reflect the real-time Oil Well data and the fault diagnosis result. In remote monitoring center, the fault diagnosis station takes the responsibility for the fault detection and diagnosis of Oil pumping units by means of neural networks and evolutionary computation. This solution accomplishes network share of Oil Well information, improves the efficiency of system development. The system has already been applied successfully to an Oil field, and has got the anticipated results
