Research on security defense model of oil & gas gathering and transferring scada based on factor neural network

2015 ◽  
pp. 31-36
Keyword(s):  
Neural Network ◽  
Defense Model ◽  
Oil Gas

Real-Time Multiphase Metering Using Non-Intrusive Microwave Sensor

2001 ◽  
Author(s):  
B. Hogan ◽  
A. I. Al-Shamma’a ◽  
J. Lucas
Keyword(s):  
Neural Network ◽  
Dielectric Properties ◽  
Real Time ◽  
Stream Flow ◽  
Microwave Cavity ◽  
Pump Speed ◽  
Microwave Sensor ◽  
Wide Range ◽  
The University ◽  
Oil Gas

Abstract At the University of Liverpool we have developed a real-time, non-intrusive multiphase dielectric meter capable of measuring the dielectric properties of different mixtures of oil, gas and water in full well stream flow. The design of such a microwave cavity using a range of rf and microwave is described. The experimental results with a wide range of multiphase mixtures from 0 to 100% are reported. In this paper we also present the implementation of a neural network to simulate the response of the meter under all conceivable conditions. Other parameters including pump speed, temperature, salt and sand are also discussed.

A Bayesian Regularized Artificial Neural Network for the Estimation of the Ignition Probability in Accidents in Oil & Gas Plants

2021 ◽  
Author(s):  
Francesco Di Maio ◽  
Oscar Scapinello ◽  
Enrico Zio ◽  
Salvatore Cincotta ◽  
Anna Crivellari ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Ignition Probability ◽  
Artificial Neural ◽  
Oil Gas ◽  
Gas Plants

Temperature Error Compensation New Method of MFL Sensor to Oil-Gas Pipeline Corrosion Inspection

2011 ◽  
Vol 204-210 ◽  
pp. 1026-1030
Author(s):  
Zhong Li Ma ◽  
Hong Da Liu
Keyword(s):  
Neural Network ◽  
Magnetic Flux ◽  
Immune Algorithm ◽  
Gas Pipeline ◽  
Artificial Immune ◽  
Functional Link ◽  
Artificial Immune Algorithm ◽  
Hall Sensors ◽  
Temperature Errors ◽  
Oil Gas

Magnetic flux leakage (MFL) inspection is a common method for detecting inner corrosions of oil-gas pipelines; the Hall Effect element is the core sensor of MFL inspections. The Hall sensor is sensitive to temperature, so environmental changes will lead to output error of Hall sensors. In order to compensate for temperature errors of Hall sensors, a segment of oil-gas pipeline with diameter 6 inch was taken as a research object. A fusion model including 50 Hall sensors and 1 temperature sensor was built up, and a functional link artificial neural network optimized by the artificial immune algorithm was used to fuse the output data of the multiple sensors. The lab research results show that the artificial immune algorithm can improve training speed and precision of the functional link neural network; the model built up can compensate effectively for temperature errors of Hall sensors and under the conditions of changing temperature, the precision of magnetic flux inspections of pipelines to detect corrosions can be improved significantly.

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