A two-stage optimization method for unmanned aerial vehicle inspection of an oil and gas pipeline network

Oil and gas pipeline networks are a key link in the coordinated development of oil and gas both upstream and downstream.To improve the reliability and safety of the oil and gas pipeline network, inspections are implemented to minimize the risk of leakage, spill and theft, as well as documenting actual incidents. In recent years, unmanned aerial vehicles have been recognized as a promising option for inspection due to their high efficiency. However, the integrated optimization of unmanned aerial vehicle inspection for oil and gas pipeline networks, including physical feasibility, the performance of mission, cooperation, real-time implementation and three-dimensional(3-D) space, is a strategic problem due to its large-scale,complexity as well as the need for efficiency. In this work, a novel mixed-integer nonlinear programming model is proposed that takes into account the constraints of the mission scenario and the safety performance of unmanned aerial vehicles. To minimize the total length of the inspection path, the model is solved by a two-stage solution method. Finally, a virtual pipeline network and a practical pipeline network are set as two examples to demonstrate the performance of the optimization schemes. Moreover, compared with the traditional genetic algorithm and simulated annealing algorithm, the self-adaptive genetic simulated annealing algorithm proposed in this paper provides strong stability.

Fuzzy Reliability Analysis for Seabed Oil Gas Pipeline Networks Under Earthquakes

The seabed oil gas pipeline network is simplified to a network with stochastic edge weight by means of the fuzzy graphics theory. With the help of network analysis, fuzzy mathematics, and stochastic theory, the problem of reliability analysis for the seabed oil gas pipeline network under earthquakes is transformed into the calculation of the transitive closure of fuzzy matrix of the stochastic fuzzy network. In classical network reliability analysis, the node is supposed to be non invalidated; in this paper, this premise is modified by introducing a disposal method which has taken the possible invalidated node into account. A good result is obtained by use of the Monte Carlo simulation analysis.

Numerical simulation of a gas pipeline network using computational fluid dynamics simulators

This article describes numerical simulation of gas pipeline network operation using high-accuracy computational fluid dynamics (CFD) simulators of the modes of gas mixture transmission through long, multi-line pipeline systems (CFD-simulator). The approach used in CFD-simulators for modeling gas mixture transmission through long, branched, multi-section pipelines is based on tailoring the full system of fluid dynamics equations to conditions of unsteady, non-isothermal processes of the gas mixture flow. Identification, in a CFD-simulator, of safe parameters for gas transmission through compressor stations amounts to finding the interior points of admissible sets described by systems of nonlinear algebraic equalities and inequalities. Such systems of equalities and inequalities comprise a formal statement of technological, design, operational and other constraints to which operation of the network equipment is subject. To illustrate the practicability of the method of numerical simulation of a gas transmission network, we compare computation results and gas flow parameters measured on-site at the gas transmission enter-prise.

Risk analysis and maintenance decision making of natural gas pipelines with external corrosion based on Bayesian network

Buried natural gas pipelines are vulnerable to external corrosion because they are encased in a soil environment for a long time.Identifying the causes of external corrosion and taking specific maintenance measures is essential.In this work,a risk analysis and maintenance decision-making model for natural gas pipelines with external corrosion is proposed based on a Bayesian network.A fault tree model is first employed to identify the causes of external corrosion.The Bayesian network for risk analysis is determined accordingly.The maintenance strategies are then inserted into the Bayesian network to show a reduction of the risk.The costs of maintenance strategies and the reduced risk after maintenance are combined in an optimization function to build a decision-making model.Because of the limitations of historical data,some of the parameters in the Bayesian network are obtained from a probabilistic estimation model,which combines expert experience and fuzzy set theory.Finally,a case study is carried out to verify the feasibility of the maintenance decision model.This indicates that the method proposed in this work can be used to provide effective maintenance schemes for different pipeline external corrosion scenarios and to reduce the possible losses caused by external corrosion.

Development of an integrated dynamic model for supply security and resilience analysis of natural gas pipeline network systems

An integrated dynamic model of natural gas pipeline networks is developed in this paper.Components for gas supply,e.g.,pipelines,junctions,compressor stations,LNG terminals,regulation stations and gas storage facilities are included in the model.These components are firstly modeled with respect to their properties and functions and,then,integrated at the system level by Graph Theory.The model can be used for simulating the system response in different scenarios of operation,and evaluate the consequences from the perspectives of supply security and resilience.A case study is considered to evaluate the accuracy of the model by benchmarking its results against those from literature and the software Pipeline Studio.Finally,the model is applied on a relatively complex natural gas pipeline network and the results are analyzed in detail from the supply security and resilience points of view.The main contributions of the paper are:firstly,a novel model of a complex gas pipeline network is proposed as a dynamic state-space model at system level;a method,based on the dynamic model,is proposed to analyze the security and resilience of supply from a system perspective.

Research on Optimization Operation of Urban Gas Pipeline Network

The optimiz at ion operation of gas pipeline network is investigated in this paper. Based on th e theories of system optimization and the multi object decision, a mathematical model about the multi object optimization operation of gas pipeline network is established, in line with the demand of urban gas pipeline network operation. A t the same time, an effective solution of the mathematical model is presented. A calculating software about optimization operation is compiled, coupling the actual operation of gas pipeline network. It can be applied to the operation of the gas pipeline network. The software was examined by real examples. The resul ts indicated that 2.13% energy consumption and 3.12% gas supply cost can be reduced through optimization operation.

Simulation of natural gas transmission pipeline network system performance

Simulation has proven to be an effective tool for analyzing pipeline network systems （PNS） in order to determine the design and operational variables which are essential for evaluating the performance of the system. This paper discusses the use of simulation for performance analysis of transmission PNS. A simulation model was developed for determining flow and pressure variables for different configuration of PNS. The mathematical formulation for the simulation model was derived based on the principles of energy conservation, mass balance, and compressor characteristics. For the determination of the pressure and flow variables, solution procedure was developed based on iterative Newton Raphson scheme and implemented using visual C＋＋6. Evaluations of the simulation model with the existing pipeline network system showed that the model enabled to determine the operational variables with less than ten iterations. The performances of the compressor working in the pipeline network system xvhich includes energy consumption, compression ratio and discharge pressure were evaluated to meet pressure requirements ranging from 4000-5000 kPa at various speed. Results of the analyses from the simulation indicated that the model could be used for performance analysis to assist decisions regarding the design and optimal operations of transmission PNS.

Study on Simulation Method of Pipeline Networks' Dynamic Characteristic in Hydraulic Manifold Block

In the design of Hydraulic Manifold Blocks (HMB), dynamic performance of inner pipeline networks usually should be evaluated. To meet the design requirements, dynamic characteristic simulation is often needed. Based on comprehensive study on the existing simulation methods, a new method combined of Power Bond Graph(PBG) and Computational Fluid Dynamic (CFD) is proposed. In this method, flow field of typical channels inside HMB is analyzed with CFD to obtain the local resistance coefficients. Then, with these coefficients, a new sectional lumped-parameter model including kinetic friction factor is developed using PBG. A typical HMB design example is given and the comparison between the simulation and the experimental results demonstrates the feasibility and effectiveness of the proposed method.

A Hybrid Encryption Algorithm for Security Enhancement of Wireless Sensor Networks:A Supervisory Approach to Pipelines

Transmission pipelines are vulnerable to various accidents and acts of vandalism.Therefore,a reliable monitoring system is needed to secure the transmission pipelines.A wireless sensor network is a wireless network consisting of distributed devices distributed at various distances,which monitors the physical and environmental conditions using sensors.Wireless sensor networks have many uses,including the built-in sensor on the outside of the pipeline or installed to support bridge structures,robotics,healthcare,environmental monitoring,etc.Wireless Sensor networks could be used to monitor the temperature,pressure,leak detection and sabotage of transmission lines.Wireless sensor networks are vulnerable to various attacks.Cryptographic algorithms have a good role in information security for wireless sensor networks.Now,various types of cryptographic algorithms provide security in networks,but there are still some problems.In this research,to improve the power of these algorithms,a new hybrid encryption algorithm for monitoring energy transmission lines and increasing the security of wireless sensor networks is proposed.The proposed hybrid encryption algorithm provides the security and timely transmission of data in wireless sensor networks to monitor the transmission pipelines.The proposed algorithm fulfills three principles of cryptography:integrity,confidentiality and authentication.The details of the algorithm and basic concepts are presented in such a way that the algorithm can be operational.

Two-stage robust optimization of power cost minimization problem in gunbarrel natural gas networks by approximate dynamic programming

In short-term operation of natural gas network,the impact of demand uncertainty is not negligible.To address this issue we propose a two-stage robust model for power cost minimization problem in gunbarrel natural gas networks.The demands between pipelines and compressor stations are uncertain with a budget parameter,since it is unlikely that all the uncertain demands reach the maximal deviation simultaneously.During solving the two-stage robust model we encounter a bilevel problem which is challenging to solve.We formulate it as a multi-dimensional dynamic programming problem and propose approximate dynamic programming methods to accelerate the calculation.Numerical results based on real network in China show that we obtain a speed gain of 7 times faster in average without compromising optimality compared with original dynamic programming algorithm.Numerical results also verify the advantage of robust model compared with deterministic model when facing uncertainties.These findings offer short-term operation methods for gunbarrel natural gas network management to handle with uncertainties.

A Novel Parameter-Optimized Recurrent Attention Network for Pipeline Leakage Detection

Accurate detection of pipeline leakage is essential to maintain the safety of pipeline transportation.Recently,deep learning(DL)has emerged as a promising tool for pipeline leakage detection(PLD).However,most existing DL methods have difficulty in achieving good performance in identifying leakage types due to the complex time dynamics of pipeline data.On the other hand,the initial parameter selection in the detection model is generally random,which may lead to unstable recognition performance.For this reason,a hybrid DL framework referred to as parameter-optimized recurrent attention network(PRAN)is presented in this paper to improve the accuracy of PLD.First,a parameter-optimized long short-term memory(LSTM)network is introduced to extract effective and robust features,which exploits a particle swarm optimization(PSO)algorithm with cross-entropy fitness function to search for globally optimal parameters.With this framework,the learning representation capability of the model is improved and the convergence rate is accelerated.Moreover,an anomaly-attention mechanism(AM)is proposed to discover class discriminative information by weighting the hidden states,which contributes to amplifying the normalabnormal distinguishable discrepancy,further improving the accuracy of PLD.After that,the proposed PRAN not only implements the adaptive optimization of network parameters,but also enlarges the contribution of normal-abnormal discrepancy,thereby overcoming the drawbacks of instability and poor generalization.Finally,the experimental results demonstrate the effectiveness and superiority of the proposed PRAN for PLD.

Convolutional Neural Network-based Leakage Detection of Crude Oil Transmission Pipes

Due to the rapid development in the petroleum industry,the leakage detection of crude oil transmission pipes has become an increasingly crucial issue.At present,oil plants at home and abroad mostly use manual inspection method for detection.This traditional method is not only inefficient but also labor-intensive.The present paper proposes a novel convolutional neural network(CNN)architecture for automatic leakage level assessment of crude oil transmission pipes.An experimental setup is developed,where a visible camera and a thermal imaging camera are used to collect image data and analyze various leakage conditions.Specifically,images are collected from various pipes with no leaking and different leaking states.Apart from images from existing pipelines,images are collected from the experimental setup with different types of joints to simulate leakage conditions in the real world.The main contributions of the present paper are,developing a convolutional neural network to classify the information in red-green-blue(RGB)and thermal images,development of the experimental setup,conducting leakage experiments,and analyzing the data using the developed approach.By successfully combining the two types of images,the proposed method is able to achieve a higher classification accuracy,compared to other methods that use RGB images or thermal images alone.Especially,compared with the method that uses thermal images only,the accuracy increases from about 91%to over 96%.

Spatial analysis for underground pipeline network information system

It is appropriate to establish underground pipeline network information system based on MapInfo software platform in many enterprises when taking account of the firm size and data amount. Since some functions of MapInfo in spatial analysis are not very strong relatively, it is difficult for MapInfo to fulfill some common functions about pipeline analysis such as spatial configuration, three-dimensional display, pipe exploding and so on. The thought and arithmetic to solve the above problems are approached based on respect theories of computer graphics and graph theory. A variety of function moduli have developed by means of senior computer languages and the system integration is realized.

GIS Enabled Condition Based Asset Management of Gas Distribution Network: A Case Study of Shahdara, Lahore Pakistan

World energy consumption increased by 56 percent, from 524 quadrillion Btu in 2010 to 820 quadrillion Btu in 2040. The increased demand in energy consumption is fulfilled by different renewable and non-renewable sources such as petroleum, natural gas, electricity, nuclear etc. Natural gas is one of the most important sources of energy. SNGPL has been managing a 94,263 km long gas pipelines network covering approximately the northern part of Pakistan. In this paper we have presented the use of condition based maintenance (CBM) management techniques with a?geographical information system (GIS) for asset management of a gas distribution network of?SNGPL. The continuous monitoring and updating of asset data reveal where the assets are located and which needs maintenance or which lies in critical condition. The system helps to save time and reduce visits to the sites and labour reduction.

Pipeline Scheduling Based on Constructive Interference in Strip Wireless Sensor Networks

Strip Wireless Sensor Networks(SWSNs)have drawn much attention in many applications such as monitoring rivers,highways and coal mines.Packet delivery in SWSN usually requires a large number of multi-hop transmissions which leads to long transmission latency in low-duty-cycle SWSNs.Several pipeline scheduling schemes have been proposed to reduce latency.However,when communication links are unreliable,pipeline scheduling is prone to failure.In this paper,we propose a pipeline scheduling transmission protocol based on constructive interference.The protocol first divides the whole network into multiple partitions and uses a pipelined mechanism to allocate active time slots for each partition.The nodes in the same partition wake up at the same time for concurrent transmission.Multiple identical signals interfere constructively at the receiver node,which enhances received signal strength and improves link quality.Simulations show that the proposed scheme can significantly reduce the transmission latency while maintaining low energy consumption compared with other schemes.

Computational Tools to Support Ethanol Pipeline Network Design Decisions

This paper considers the pipeline network design problem (PND) in ethanol transportation, with a view to providing robust and efficient computational tools to assist decision makers in evaluating the technical and economic feasibility of ethanol pipeline network designs. Such tools must be able to address major design decisions and technical characteristics, and estimate network construction and operation costs to any time horizon. The specific context in which the study was conducted was the ethanol industry in Sao Paulo. Five instances were constructed using pseudo-real data to test the methodologies developed.

Prediction on Failure Pressure of Pipeline Containing Corrosion Defects Based on ISSA-BPNNModel

Oil and gas pipelines are affected by many factors,such as pipe wall thinning and pipeline rupture.Accurate prediction of failure pressure of oil and gas pipelines can provide technical support for pipeline safety management.Aiming at the shortcomings of the BP Neural Network(BPNN)model,such as low learning efficiency,sensitivity to initial weights,and easy falling into a local optimal state,an Improved Sparrow Search Algorithm(ISSA)is adopted to optimize the initial weights and thresholds of BPNN,and an ISSA-BPNN failure pressure prediction model for corroded pipelines is established.Taking 61 sets of pipelines blasting test data as an example,the prediction model was built and predicted by MATLAB software,and compared with the BPNN model,GA-BPNN model,and SSA-BPNN model.The results show that the MAPE of the ISSA-BPNN model is 3.4177%,and the R2 is 0.9880,both of which are superior to its comparison model.Using the ISSA-BPNN model has high prediction accuracy and stability,and can provide support for pipeline inspection and maintenance.

基于SynerGEE Gas的蒸汽管网模拟技术应用

本文介绍了国内外蒸汽管网模拟技术的研究与应用现状,阐述了应用SynerGEE Gas软件对蒸汽管网进行仿真模拟的基本原理与方法。以天津空港经济区蒸汽管网仿真模型的构建为例,通过将仿真结果与实测数据的对比分析得出结论,该软件模拟误差小,仿真精度高,对实际生产有较好的指导作用。

PIPELINE软件在气田集输实时监控中的应用

PIPELINE软件是一种离线或者在线的集输管网仿真模拟计算软件,不但可以用来测试和评价输气管道的设计或进行实际操作参数的设置,还能与气田集输实时监控系统进行有机结合,为集输管网运行提供辅助专家诊断分析。以大牛地气田集输生产实际为例,进行管网水力、热力计算,天然气调峰正常工况分析和用户中断用气工况模拟分析,结果表明,该仿真模拟软件和实时监控系统有机结合,改变了气田根据人工计算或凭经验来预测天然气集输管网的运行情况和调峰的现状,不但降低了劳动强度,而且使预测效果得到了根本性提升,排除了管网运行管理的安全隐患。PIPELINE软件不仅方便了管网的运营管理,还避免了生产调度上的盲目性,为科学地管理气田集输管网系统提供了专家式的理论指导。

An optimal flow rate allocation model of the oilfield treated oil pipeline network

Oilfield treated oil pipeline network is the link connecting the upstream oilfields and the downstream refineries.Due to the differences in operating costs and transportation fee between different pipelines and the fluctuation in the demand and sales prices of the treated oil,there is an optimal flow allocation plan for the pipeline network to make the oilfield company obtain the highest social and economic benefit.In this study,a mixed integer nonlinear programming(MINLP)model is developed to determine the optimal flow rate allocation plan of the large-scale and complex treated oil pipeline network,and both the social and economic benefits are considered simultaneously.The optimization objective is the multi-objective which includes the largest user satisfaction and the highest economic benefit.The model constraints include the oilfield production capacity,refinery demand,pipeline transmission capacity,flow,pressure,and temperature of the node and station,and the pipeline hydraulic and thermal calculations.Python 3.7 is utilized for the programming of the off-line calculation procedure and the MINLP model,and GUROBI 9.0.2 is served as the MINLP solver.Moreover,the model is applied to a real treated oil pipeline network located in China,and three optimization scenarios are analyzed.For social benefit,the values of the user satisfaction of each refinery and the total network are 1 before and after optimization for scenarios 1,2,and 3.For economic benefit,the annual revenue can be increased by 0.227,0.293,and 0.548 billion yuan after the optimization in scenario 1,2,and 3,respectively.