scholarly journals Distributed Vibration Sensing System for Oil and Gas Pipelines Based on COTDR and BP Neural Network

2018 ◽  
Author(s):  
Qizhong Yan ◽  
Yue Yang ◽  
Zheng Zhang ◽  
Shan Jiang ◽  
Yimin XU ◽  
...  
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Oil And Gas ◽  
Gas Pipelines ◽  
Sensing System ◽  
Oil And Gas Pipelines ◽  
Vibration Sensing

scholarly journals Application of the LM-BP neural network approach for landslide risk assessments

2018 ◽  
Author(s):  
Junnan Xiong ◽  
Ming Sun ◽  
Hao Zhang ◽  
Weiming Cheng ◽  
Yinghui Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Risk Assessment ◽  
Hazard Assessment ◽  
Bp Neural Network ◽  
Vulnerability Assessment ◽  
Oil And Gas ◽  
Gas Pipelines ◽  
Long Distance ◽  
Oil Pipeline ◽  
Oil And Gas Pipelines

Abstract. Landslide disaster is one of the main risks involved with the operation of long-distance oil and gas pipelines. Because previously established disaster risk models are too subjective, this paper presents a quantitative model for regional risk assessment through an analysis of the laws of historical landslide disasters along oil and gas pipelines. Using the Guangyuan section of the Lanzhou–Chengdu–Chongqing (LCC) Long-Distance Products Oil Pipeline (82 km) in China as a case study, we successively carried out two independent assessments: a hazard assessment and a vulnerability assessment. We used an entropy weight method to establish a system for the vulnerability assessment, whereas a Levenberg Marquardt-Back Propagation (LM-BP) neural network model was used to conduct the hazard assessment. The risk assessment was carried out on the basis of two assessments. The first, the system of the vulnerability assessment, considered the pipeline position and the angle between the pipe and the landslide (pipeline laying environmental factors). We also used an interpolation theory to generate the standard sample matrix of the LM-BP neural network. Accordingly, a landslide hazard risk zoning map was obtained based on hazard and vulnerability assessment. The results showed that about 70 % of the slopes were in high-hazard areas with a comparatively high landslide possibility and that the southern section of the oil pipeline in the study area was in danger. These results can be used as a guide for preventing and reducing regional hazards, establishing safe routes for both existing and new pipelines and safely operating pipelines in the Guangyuan section and other segments of the LCC oil pipeline.

scholarly journals Pipeline Bending Strain Measurement and Compensation Technology Based on Wavelet Neural Network

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Rui Li ◽  
Maolin Cai ◽  
Yan Shi ◽  
Qingshan Feng ◽  
Shucong Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Oil And Gas ◽  
Original Method ◽  
Wavelet Neural Network ◽  
Measurement Unit ◽  
Gas Pipelines ◽  
Long Distance ◽  
Pipeline Inspection ◽  
Bending Strain ◽  
Oil And Gas Pipelines

The bending strain of long distance oil and gas pipelines may lead to instability of the pipeline and failure of materials, which seriously deteriorates the transportation security of oil and gas. To locate the position of the bending strain for maintenance, an Inertial Measurement Unit (IMU) is usually adopted in a Pipeline Inspection Gauge (PIG). The attitude data of the IMU is usually acquired to calculate the bending strain in the pipe. However, because of the vibrations in the pipeline and other system noises, the resulting bending strain calculations may be incorrect. To improve the measurement precision, a method, based on wavelet neural network, was proposed. To test the proposed method experimentally, a PIG with the proposed method is used to detect a straight pipeline. It can be obtained that the proposed method has a better repeatability and convergence than the original method. Furthermore, the new method is more accurate than the original method and the accuracy of bending strain is raised by about 23% compared to original method. This paper provides a novel method for precisely inspecting bending strain of long distance oil and gas pipelines and lays a foundation for improving the precision of inspection of bending strain of long distance oil and gas pipelines.

scholarly journals Neural Network Model for Predicting the Condition of Oil and Gas Pipelines

2019 ◽  
Vol 3 (122) ◽  
pp. 52-56
Author(s):  
T. E. Arteeva ◽  
◽  
N. D. Tsyganenko ◽  
R. D. Tatlyev ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Oil And Gas ◽  
Gas Pipelines ◽  
Oil And Gas Pipelines

scholarly journals Application of the Levenburg–Marquardt back propagation neural network approach for landslide risk assessments

2019 ◽  
Vol 19 (3) ◽  
pp. 629-653 ◽  
Author(s):  
Junnan Xiong ◽  
Ming Sun ◽  
Hao Zhang ◽  
Weiming Cheng ◽  
Yinghui Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Risk Assessment ◽  
Bp Neural Network ◽  
Vulnerability Assessment ◽  
Oil And Gas ◽  
Back Propagation ◽  
Susceptibility Assessment ◽  
Long Distance ◽  
Oil Pipeline ◽  
Oil And Gas Pipelines

Abstract. Landslide disasters are one of the main risks involved with the operation of long-distance oil and gas pipelines. Because previously established disaster risk models are too subjective, this paper presents a quantitative model for regional risk assessment through an analysis of the patterns of historical landslide disasters along oil and gas pipelines. Using the Guangyuan section of the Lanzhou–Chengdu–Chongqing (LCC) long-distance multiproduct oil pipeline (82 km) in China as a case study, we successively carried out two independent assessments: a susceptibility assessment and a vulnerability assessment. We used an entropy weight method to establish a system for the vulnerability assessment, whereas a Levenberg–Marquardt back propagation (LM-BP) neural network model was used to conduct the susceptibility assessment. The risk assessment was carried out on the basis of two assessments. The first, the system of the vulnerability assessment, considered the pipeline position and the angle between the pipe and the landslide (pipeline laying environmental factors). We also used an interpolation theory to generate the standard sample matrix of the LM-BP neural network. Accordingly, a landslide susceptibility risk zoning map was obtained based on susceptibility and vulnerability assessment. The results show that about 70 % of the slopes were in high-susceptibility areas with a comparatively high landslide possibility and that the southern section of the oil pipeline in the study area was in danger. These results can be used as a guide for preventing and reducing regional hazards, establishing safe routes for both existing and new pipelines, and safely operating pipelines in the Guangyuan area and other segments of the LCC oil pipeline.

scholarly journals Research on Apparent Resistivity Imaging of Transient Electromagnetic Method for Oil and Gas Pipelines Based on GA-BP Neural Network

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Zheng Liang ◽  
Bao Tian ◽  
Liang Zhang
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Bp Neural Network ◽  
Apparent Resistivity ◽  
Oil And Gas ◽  
Bp Network ◽  
Imaging Technology ◽  
Resistivity Imaging ◽  
Transient Electromagnetic ◽  
Oil And Gas Pipelines

Transient electromagnetic apparent resistivity imaging technology is one of the more promising methods for external inspection of metallic oil and gas pipelines. Through the research on the transient electromagnetic response and imaging technology of pipelines, it is found that the accuracy and real-time performance of the apparent resistivity calculation are the key to its application. To achieve fast imaging, a three-layer BP neural network is designed with the kernel function of the secondary field as the input and the transient parameter value as the output; the nonlinear equation of transient response is fitted by the neural network to solve the apparent resistivity, and inversion depth is calculated based on smoke ring theory. Aiming at the shortcomings of the traditional BP network, such as slow convergence rate and the ease of falling into local minima, the genetic algorithm is designed to optimize the initial weight and threshold of the network. In the model pipeline experiment, the measured data are brought into the trained GA-BP network, and calculation time is greatly shortened. The obtained sectional image can directly and accurately reflect the pipeline shape. The validity and practicability of the transient electromagnetic apparent resistivity imaging technology based on the GA-BP neural network are verified, which is expected to be a powerful tool for real-time evaluation of pipeline corrosion detection.

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