Dynamical mechanisms of effects of landslides on long distance oil and gas pipelines

2006 ◽  
Vol 11 (4) ◽  
pp. 820-824 ◽  
Author(s):  
Ma Qingwen ◽  
Wang Chenghua ◽  
Kong Jiming
Keyword(s):  
Oil And Gas ◽  
Gas Pipelines ◽  
Long Distance ◽  
Oil And Gas Pipelines

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 Optimization of distribution of emergency resources for emergency rescue points of oil and gas pipelines

2021 ◽  
Vol 266 ◽  
pp. 01016
Author(s):  
Y.Y. Chen ◽  
L.B. Zhang ◽  
J.Q. Hu ◽  
Z.Y. Liu
Keyword(s):  
Resource Allocation ◽  
Oil And Gas ◽  
Resources Allocation ◽  
Gas Pipelines ◽  
Long Distance ◽  
Emergency Rescue ◽  
Oil And Gas Pipelines ◽  
Fixed Proportion ◽  
Optimized Allocation ◽  
Resource Shortage

According to the inherent characteristics of long-distance oil and gas pipelines, the optimization of emergency resources allocation can be implemented to maximize the utilization of pipeline emergency resources under a certain cost of emergency investment. We built an improved solution of a multiple knapsack problem in a greedy algorithm, proposed maximizing Emergency Resources Factor (ERF) as the greedy strategy, and established the optimization model of emergency resources allocation. This model innovatively combines factors such as the centrality of rescue points, the risk of pipe sections, and the necessity of emergency resources. The results show that, compared to a conventional resource allocation in a fixed proportion, an optimized allocation can reduce resource shortage and redundancy by 2.660% and 1.051%, respectively. Therefore, this model can be used to control the initialization of resource allocation in emergency rescue points of long-distance oil and gas pipelines.

First Chinese SCADA System for Long Distance Oil and Gas Pipelines

2008 ◽  
Author(s):  
Xia Yufei ◽  
Wai Tse ◽  
Xinyan Qin ◽  
Sheng Liu ◽  
Sen Zhang
Keyword(s):  
Joint Venture ◽  
Oil And Gas ◽  
Gas Pipelines ◽  
System Testing ◽  
Long Distance ◽  
Scada System ◽  
Oil And Gas Pipelines ◽  
The Future ◽  
Scada Systems ◽  
Insight Into

Up until 2007, China has been relying on foreign SCADA vendors to provide SCADA systems for the controlling and monitoring of long distance oil and gas pipelines. While foreign SCADA systems satisfy most requirements, there are still issues such as high cost, limited localization, lack of experienced local representatives, and slow to respond to system changes, etc., remain to be solved and improved in the future. In Jan 2006, Sinopec, one of the major oil and gas corporations in China, initiated a joint venture with a local IT company, Beijing East Mainstay Technology Company Ltd. (East MS), to develop a SCADA system for oil and gas pipelines. The first prototype was finished in Aug 2006. It then went through 4 months of rigorous system testing and refinement and finally it was applied to 5 refined products pipelines in China. This paper unfolds the architecture of the system and discusses the system features in detail to give the audience an insight into the first Chinese SCADA system for long distance oil and gas pipelines.

Application of HAZOP Study in Key SOP of Oil and Gas Pipelines

2013 ◽  
Vol 827 ◽  
pp. 112-117
Author(s):  
Deng Feng Zheng
Keyword(s):  
Operating Procedure ◽  
Oil And Gas ◽  
Gas Pipeline ◽  
Control Measures ◽  
Pipeline System ◽  
Gas Pipelines ◽  
Long Distance ◽  
Accident Rate ◽  
Oil And Gas Pipelines ◽  
Operation Procedure

For long distance oil and gas pipeline system has the characteristics of high accident rate tending to happen in personnel job activity, this paper expounds the necessity and application steps of HAZOP (Hazard and Operability Analysis) analysis for long-distance pipeline system key operation procedure (SOP). The HAZAOP analysis of deviation, causes of deviation, consequences, existing control measures and recommending measures, make the key operating procedure safer, and also contributes to the improvement of the oil and gas pipeline system safety and fitness between operating procedures and hardware facilities. The results of application examples show that HAZOP is able to identify key operating procedure loopholes, helps enterprises optimize key operating procedures, improves the hardware facilities, and has an important role to improve the security of the key operation of oil and gas pipelines.

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.

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