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 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.

Research on Closed-Loop Safety Production System of Hot Oil Pipeline Based on Big Data Mining

2019 ◽  
Author(s):  
Yu Tao ◽  
Li Chuanxian ◽  
Liu Lijun ◽  
Chen Hongjun ◽  
Guo Peng ◽  
...  
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Big Data ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Closed Loop ◽  
Long Distance ◽  
Oil Pipeline ◽  
Big Data Mining ◽  
Actual Production

Abstract The process of long-distance hot oil pipeline is complicated, and its safety and optimization are contradictory. In actual production and operation, the theoretical calculation model of oil temperature along the pipeline has some problems, such as large error and complex application. This research relies on actual production data and uses big data mining algorithms such as BP neural network, ARMA, seq2seq to establish oil temperature prediction model. The prediction result is less than 0.5 C, which solves the problem of accurate prediction of dynamic oil temperature during pipeline operation. Combined with pigging, the friction prediction model of standard pipeline section is established by BP neural network, and then the economic pigging period of 80 days is given; and after the friction database is established, the historical friction data are analyzed by using the Gauss formula, and 95% of the friction is set as the threshold data to effectively monitor the variation of the friction due to the long period of waxing in pipelines. The closed loop operation system of hot oil pipeline safety and optimization was formed to guide the daily process adjustment and production arrangement of pipeline with energy saving up to 92.4%. The prediction model and research results based on production big data have good adaptability and generalization, which lays a foundation for future intelligent control of pipelines.

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.

Economic benefit of shale gas exploitation based on back propagation neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8823-8830
Author(s):  
Jiafeng Li ◽  
Hui Hu ◽  
Xiang Li ◽  
Qian Jin ◽  
Tianhao Huang
Keyword(s):  
Neural Network ◽  
Shale Gas ◽  
Bp Neural Network ◽  
Large Scale ◽  
Linear Prediction ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Economic Benefits ◽  
Gas Well ◽  
Well Production

Under the influence of COVID-19, the economic benefits of shale gas development are greatly affected. With the large-scale development and utilization of shale gas in China, it is increasingly important to assess the economic impact of shale gas development. Therefore, this paper proposes a method for predicting the production of shale gas reservoirs, and uses back propagation (BP) neural network to nonlinearly fit reservoir reconstruction data to obtain shale gas well production forecasting models. Experiments show that compared with the traditional BP neural network, the proposed method can effectively improve the accuracy and stability of the prediction. There is a nonlinear correlation between reservoir reconstruction data and gas well production, which does not apply to traditional linear prediction methods

scholarly journals Assessment of water resource security in karst area of Guizhou Province, China

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Liying Liu
Keyword(s):  
Neural Network ◽  
Water Resources ◽  
Water Resource ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Water Shortage ◽  
Guizhou Province ◽  
Karst Area ◽  
Water Resource Security ◽  
Resource Security

AbstractThis paper presents the assessment of water resource security in the Guizhou karst area, China. A mean impact value and back-propagation (MIV-BP) neural network was used to understand the influencing factors. Thirty-one indices involving five aspects, the water quality subsystem, water quantity subsystem, engineering water shortage subsystem, water resource vulnerability subsystem, and water resource carrying capacity subsystem, were selected to establish an evaluation index of water resource security. In addition, a genetic algorithm and back-propagation (GA-BP) neural network was constructed to assess the water resource security of Guizhou Province from 2001 to 2015. The results show that water resource security in Guizhou was at a moderate warning level from 2001 to 2006 and a critical safety level from 2007 to 2015, except in 2011 when a moderate warning level was reached. For protection and management of water resources in a karst area, the modes of development and utilization of water resources must be thoroughly understood, along with the impact of engineering water shortage. These results are a meaningful contribution to regional ecological restoration and socio-economic development and can promote better practices for future planning.

Study on the Generalized Holo-Factors Mathematical Model of Dimension-Error and Shape-Error for Sheet Metal in Stamping Based on the Back Propagation (BP) Neural Network

2016 ◽  
Vol 138 (6) ◽  
Author(s):  
Lizhi Gu ◽  
Tianqing Zheng
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Sheet Metal ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Shape Error ◽  
Back Propagation Algorithm ◽  
Minor Strain ◽  
Major Strain ◽  
Forming Precision

Precision improvement in sheet metal stamping has been the concern that the stamping researchers have engaged in. In order to improve the forming precision of sheet metal in stamping, this paper devoted to establish the generalized holo-factors mathematical model of dimension-error and shape-error for sheet metal in stamping based on BP neural network. Factors influencing the forming precision of stamping sheet metal were divided, altogether ten factors, and the generalized holo-factors mathematical model of dimension-error and shape-error for sheet metal in stamping was established using the back-propagation algorithm of error based on BP neural network. The undetermined coefficients of the model previously established were soluble according to the simulation data of sheet punching combined with the specific shape based on the BP neural network. With this mathematical model, the forecast data compared with the validate data could be obtained, so as to verify the fine practicability that the previously established mathematical model had, and then, it was shown that the generalized holo-factors mathematical model of size error and shape-error had fine practicality and versatility. Based on the generalized holo-factors mathematical model of error exemplified by the cylindrical parts, a group of process parameters could be selected, in which forming thickness was between 0.713 mm and 1.335 mm, major strain was between 0.085 and 0.519, and minor strain was between −0.596 and 0.319 from the generalized holo-factors mathematical model prediction, at the same time, the forming thickness, the major strain, and the minor strain were in good condition.

Applying the Quantitative Risk Assessment (QRA) to Improve Safety Management of Oil and Gas Pipeline Stations in China

2012 ◽  
Author(s):  
Wenxing Feng ◽  
Xiaoqiang Xiang ◽  
Guangming Jia ◽  
Lianshuang Dai ◽  
Yulei Gu ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Environmental Protection ◽  
Oil And Gas ◽  
Hazard Identification ◽  
Gas Pipeline ◽  
Industrial Safety ◽  
Quantitative Risk Assessment ◽  
Chinese Government ◽  
Land Resource ◽  
Oil Pipeline

The oil and gas pipeline companies in China are facing unprecedented opportunities and challenges because of China’s increasing demand for oil and gas energy that is attributed to rapid economic and social development. Limitation of land resource and the fast urbanization lead to a determinate result that many pipelines have to go through or be adjacent to highly populated areas such as cities or towns. The increasing Chinese government regulation, and public concerns about industrial safety and environmental protection push the pipeline companies to enhance the safety, health and environmental protection management. In recent years, PetroChina Pipeline Company (PPC) pays a lot of attention and effort to improve employees and public safety around the pipeline facilities. A comprehensive, integrated HSE management system is continuously improved and effectively implemented in PPC. PPC conducts hazard identification, risk assessment, risk control and mitigation, risk monitoring. For the oil and gas stations in highly populated area or with numerous employees, PPC carries out quantitative risk assessment (QRA) to evaluate and manage the population risk. To make the assessment, “Guidelines for quantitative risk assessments” (purple book) published by Committee for the Prevention of Disasters of Netherlands is used along with a software package. The basic principles, process, and methods of QRA technology are introduced in this article. The process is to identify the station hazards, determinate the failure scenarios of the facilities, estimate the possibilities of leakage failures, calculate the consequences of failures and damages to population, demonstrate the individual risk and social risk, and evaluate whether the risk is acceptable. The process may involve the mathematical modeling of fluid and gas spill, dispersion, fire and explosion. One QRA case in an oil pipeline station is described in this article to illustrate the application process and discuss several key issues in the assessment. Using QRA technique, about 20 stations have been evaluated in PPC. On the basis of the results, managers have taken prevention and mitigation plans to control the risk. QRAs in the pipeline station can provide a quantitative basis and valuable reference for the company’s decision-making and land use planning. Also, QRA can play a role to make a better relationship between the pipeline companies and the local regulator and public. Finally, this article delivers limitations of QRA in Chinese pipeline stations and discusses issues of the solutions.

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