We developed a predictive model for the pipeline friction in the 520-730 m^3/h transmission range using the multi-layerperceptron-back-propagation(MLP-BP)method and analyzing the unit friction data after the pigging o...We developed a predictive model for the pipeline friction in the 520-730 m^3/h transmission range using the multi-layerperceptron-back-propagation(MLP-BP)method and analyzing the unit friction data after the pigging of a hot oil pipeline.In view of the shortcomings of the MLP-BP model,two optimization methods,the genetic algorithm(GA)and mind evolutionary algorithm(MEA),were used to optimize the MLP-BP model.The research results were applied to the standard friction prediction of three sections of a hot oil pipeline.After the GA and MEA optimizations,the average errors of the three sections were 0.0041 MPa for the GA and 0.0012 MPa for the MEA,and the mean-square errors were 0.083 and 0.067,respectively.The MEA-BP model prediction results were characterized by high precision and small dispersion.The MEABP prediction model was applied to the analysis of the wax formation 60 and 90 days after pigging.The analysis results showed that the model can effectively guide pipe pigging and optimization.There was little sample data for the individual transmission and oil temperature steps because the model was based on actual production data modeling and analysis,which may have affected the accuracy and adaptability of the model.展开更多
Problems involving wax deposition threaten seriously crude pipelines both economically and operationally. Wax deposition in oil pipelines is a complicated problem having a number of uncertainties and indeterminations....Problems involving wax deposition threaten seriously crude pipelines both economically and operationally. Wax deposition in oil pipelines is a complicated problem having a number of uncertainties and indeterminations. The Grey System Theory is a suitable theory for coping with systems in which some information is clear and some is not, so it is an adequate model for studying the process of wax deposition. In order to predict accurately wax deposition along a pipeline, the Grey Model was applied to fit the data of wax deposition rate and the thickness of the deposited wax layer on the pipe-wall, and to give accurate forecast on wax deposition in oil pipelines. The results showed that the average residential error of the Grey Prediction Model is smaller than 2%. They further showed that this model exhibited high prediction accuracy. Our investigation proved that the Grey Model is a viable means for forecasting wax deposition. These findings offer valuable references for the oil industry and for firms dealing with wax cleaning in oil pipelines.展开更多
The Bangladeshi government signed a framework agreement with China for construction of a 220-km pipeline to carry oil from tankers in the Bay of Bengal to storage plants on Chinese mainland. Bangladesh＇s Economic Rel...The Bangladeshi government signed a framework agreement with China for construction of a 220-km pipeline to carry oil from tankers in the Bay of Bengal to storage plants on Chinese mainland. Bangladesh＇s Economic Relations Department （ERD） Secretary Kazi Shofiqul Azam and Chinese Ambassador to Bangladesh Ma Mingqiang signed the framework agreement in the capital of Dhaka.展开更多
Magnetic field and microorganisms are important factors influencing the stress corrosion cracking(SCC)of buried oil and gas pipelines. Once SCC occurs in buried pipelines, it will cause serious hazards to the soil env...Magnetic field and microorganisms are important factors influencing the stress corrosion cracking(SCC)of buried oil and gas pipelines. Once SCC occurs in buried pipelines, it will cause serious hazards to the soil environment. The SCC behavior of X80 pipeline steel under the magnetic field and sulfate-reducing bacteria(SRB) environment was investigated by immersion tests, electrochemical tests, and slow strain rate tensile(SSRT) tests. The results showed that the corrosion and SCC sensitivity of X80 steel decreased with increasing the magnetic field strength in the sterile environment. The SCC sensitivity was higher in the biotic environment inoculated with SRB, but it also decreased with increasing magnetic field strength, which was due to the magnetic field reduces microbial activity and promotes the formation of dense film layer. This work provided theoretical guidance on the prevention of SCC in pipeline steel under magnetic field and SRB coexistence.展开更多
A comprehensive and objective risk evaluation model of oil and gas pipelines based on an improved analytic hierarchy process(AHP)and technique for order preference by similarity to an ideal solution(TOPSIS)is establis...A comprehensive and objective risk evaluation model of oil and gas pipelines based on an improved analytic hierarchy process(AHP)and technique for order preference by similarity to an ideal solution(TOPSIS)is established to identify potential hazards in time.First,a barrier model and fault tree analysis are used to establish an index system for oil and gas pipeline risk evaluation on the basis of five important factors:corrosion,external interference,material/construction,natural disasters,and function and operation.Next,the index weight for oil and gas pipeline risk evaluation is computed by applying the improved AHP based on the five-scale method.Then,the TOPSIS of a multi-attribute decision-making theory is studied.The method for determining positive/negative ideal solutions and the normalized equation for benefit/cost indexes is improved to render TOPSIS applicable for the comprehensive risk evaluation of pipelines.The closeness coefficient of oil and gas pipelines is calculated by applying the improved TOPSIS.Finally,the weight and the closeness coefficient are combined to determine the risk level of pipelines.Empirical research using a long-distance pipeline as an example is conducted,and adjustment factors are used to verify the model.Results show that the risk evaluation model of oil and gas pipelines based on the improved AHP–TOPSIS is valuable and feasible.The model comprehensively considers the risk factors of oil and gas pipelines and provides comprehensive,rational,and scientific evaluation results.It represents a new decision-making method for systems engineering in pipeline enterprises and provides a comprehensive understanding of the safety status of oil and gas pipelines.The new system engineering decision-making method is important for preventing oil and gas pipeline accidents.展开更多
Oil product pipelines have features such as transporting multiple materials, ever-changing operating conditions, and synchronism between the oil input plan and the oil offloading plan. In this paper, an optimal model ...Oil product pipelines have features such as transporting multiple materials, ever-changing operating conditions, and synchronism between the oil input plan and the oil offloading plan. In this paper, an optimal model was established for a single-source multi-distribution oil pro- duct pipeline, and scheduling plans were made based on supply. In the model, time node constraints, oil offloading plan constraints, and migration of batch constraints were taken into consideration. The minimum deviation between the demanded oil volumes and the actual offloading volumes was chosen as the objective function, and a linear programming model was established on the basis of known time nodes＇ sequence. The ant colony optimization algo- rithm and simplex method were used to solve the model. The model was applied to a real pipeline and it performed well.展开更多
This paper addresses the scheduling and inventory management of a straight pipeline system connecting a single refinery to multiple distribution centers.By increasing the number of batches and time periods,maintaining...This paper addresses the scheduling and inventory management of a straight pipeline system connecting a single refinery to multiple distribution centers.By increasing the number of batches and time periods,maintaining the model resolution by using linear programming-based methods and commercial solvers would be very time-consuming.In this paper,we make an attempt to utilize the problem structure and develop a decomposition-based algorithm capable of finding near-optimal solutions for large instances in a reasonable time.The algorithm starts with a relaxed version of the model and adds a family of cuts on the fly,so that a near-optimal solution is obtained within a few iterations.The idea behind the cut generation is based on the knowledge of the underlying problem structure.Computational experiments on a real-world data case and some randomly generated instances confirm the efficiency of the proposed algorithm in terms of the solution quality and time.展开更多
s: Regarding the influencing factors in an optimal selection of pipeline design alternative as fuzzy variables with different weights, a fuzzy comprehensive assessment was applied to an optimal selection of the design...s: Regarding the influencing factors in an optimal selection of pipeline design alternative as fuzzy variables with different weights, a fuzzy comprehensive assessment was applied to an optimal selection of the design alternative. Giving the Lanzhou-Chengdu pipeline as an example to explain the process, the result shows that this method is acceptable.展开更多
In water-lubricated pipeline transportation of heavy oil and bitumen, a thin oil film typically coats the pipe wall. A detailed study of the hydrodynamic effects of this fouling layer is critical to the design and ope...In water-lubricated pipeline transportation of heavy oil and bitumen, a thin oil film typically coats the pipe wall. A detailed study of the hydrodynamic effects of this fouling layer is critical to the design and operation of oil-water pipelines, as it can increase the pipeline pressure loss （and pumping power requirements） by 15 times or more. In this study, a parametric investigation of the hydrodynamic effects caused by the wall coating of viscous oil was conducted. A custom-built rectangular flow cell was used. A validated CFD-based procedure was used to determine the hydrodynamic roughness from the measured pressure losses. A similar procedure was followed for a set of pipe loop tests. The effects of the thickness of the oil coating layer, the oil viscosity, and water flow rate on the hydrodynamic roughness were evaluated. Oil viscosities from 3 to 21300 Pa s were tested. The results show that the equivalent hydrodynamic roughness produced by a wall coating layer of viscous oil is dependent on the coating thickness but essentially independent of oil viscosity. A new correlation was developed using these data to predict the hydrodynamic roughness for flow conditions in which a viscous oil coating is produced on the pipe wall.展开更多
Since leaks in high-pressure pipelines transporting crude oil can cause severe economic losses,a reliable leak risk assessment can assist in developing an effective pipeline maintenance plan and avoiding unexpected in...Since leaks in high-pressure pipelines transporting crude oil can cause severe economic losses,a reliable leak risk assessment can assist in developing an effective pipeline maintenance plan and avoiding unexpected incidents.The fast and accurate leak detection methods are essential for maintaining pipeline safety in pipeline reliability engineering.Current oil pipeline leakage signals are insufficient for feature extraction,while the training time for traditional leakage prediction models is too long.A new leak detection method is proposed based on time-frequency features and the Genetic Algorithm-Levenberg Marquardt(GA-LM)classification model for predicting the leakage status of oil pipelines.The signal that has been processed is transformed to the time and frequency domain,allowing full expression of the original signal.The traditional Back Propagation(BP)neural network is optimized by the Genetic Algorithm(GA)and Levenberg Marquardt(LM)algorithms.The results show that the recognition effect of a combined feature parameter is superior to that of a single feature parameter.The Accuracy,Precision,Recall,and F1score of the GA-LM model is 95%,93.5%,96.7%,and 95.1%,respectively,which proves that the GA-LM model has a good predictive effect and excellent stability for positive and negative samples.The proposed GA-LM model can obviously reduce training time and improve recognition efficiency.In addition,considering that a large number of samples are required for model training,a wavelet threshold method is proposed to generate sample data with higher reliability.The research results can provide an effective theoretical and technical reference for the leakage risk assessment of the actual oil pipelines.展开更多
基金supported by National Natural Science Foundation of China(51904327,51774311)Natural Science Foundation of Shandong Province of China(ZR2017MEE022)+1 种基金China Postdoctoral Science Foundation(2019TQ0354,2019M662468)Qingdao postdoctoral researchers applied research project.
文摘We developed a predictive model for the pipeline friction in the 520-730 m^3/h transmission range using the multi-layerperceptron-back-propagation(MLP-BP)method and analyzing the unit friction data after the pigging of a hot oil pipeline.In view of the shortcomings of the MLP-BP model,two optimization methods,the genetic algorithm(GA)and mind evolutionary algorithm(MEA),were used to optimize the MLP-BP model.The research results were applied to the standard friction prediction of three sections of a hot oil pipeline.After the GA and MEA optimizations,the average errors of the three sections were 0.0041 MPa for the GA and 0.0012 MPa for the MEA,and the mean-square errors were 0.083 and 0.067,respectively.The MEA-BP model prediction results were characterized by high precision and small dispersion.The MEABP prediction model was applied to the analysis of the wax formation 60 and 90 days after pigging.The analysis results showed that the model can effectively guide pipe pigging and optimization.There was little sample data for the individual transmission and oil temperature steps because the model was based on actual production data modeling and analysis,which may have affected the accuracy and adaptability of the model.
基金Financially supported by Sinopec Corp (2001101).
文摘Problems involving wax deposition threaten seriously crude pipelines both economically and operationally. Wax deposition in oil pipelines is a complicated problem having a number of uncertainties and indeterminations. The Grey System Theory is a suitable theory for coping with systems in which some information is clear and some is not, so it is an adequate model for studying the process of wax deposition. In order to predict accurately wax deposition along a pipeline, the Grey Model was applied to fit the data of wax deposition rate and the thickness of the deposited wax layer on the pipe-wall, and to give accurate forecast on wax deposition in oil pipelines. The results showed that the average residential error of the Grey Prediction Model is smaller than 2%. They further showed that this model exhibited high prediction accuracy. Our investigation proved that the Grey Model is a viable means for forecasting wax deposition. These findings offer valuable references for the oil industry and for firms dealing with wax cleaning in oil pipelines.
文摘The Bangladeshi government signed a framework agreement with China for construction of a 220-km pipeline to carry oil from tankers in the Bay of Bengal to storage plants on Chinese mainland. Bangladesh＇s Economic Relations Department （ERD） Secretary Kazi Shofiqul Azam and Chinese Ambassador to Bangladesh Ma Mingqiang signed the framework agreement in the capital of Dhaka.
基金supported by the National Science Foundation of China(Grant numbers 52274062)Natural Science Foundation of Liaoning Province(Grant numbers 2022-MS-362)。
文摘Magnetic field and microorganisms are important factors influencing the stress corrosion cracking(SCC)of buried oil and gas pipelines. Once SCC occurs in buried pipelines, it will cause serious hazards to the soil environment. The SCC behavior of X80 pipeline steel under the magnetic field and sulfate-reducing bacteria(SRB) environment was investigated by immersion tests, electrochemical tests, and slow strain rate tensile(SSRT) tests. The results showed that the corrosion and SCC sensitivity of X80 steel decreased with increasing the magnetic field strength in the sterile environment. The SCC sensitivity was higher in the biotic environment inoculated with SRB, but it also decreased with increasing magnetic field strength, which was due to the magnetic field reduces microbial activity and promotes the formation of dense film layer. This work provided theoretical guidance on the prevention of SCC in pipeline steel under magnetic field and SRB coexistence.
基金supported by the National Key Research and Development Program of China(Grant Nos.2017YFC0805804,2017YFC0805801)
文摘A comprehensive and objective risk evaluation model of oil and gas pipelines based on an improved analytic hierarchy process(AHP)and technique for order preference by similarity to an ideal solution(TOPSIS)is established to identify potential hazards in time.First,a barrier model and fault tree analysis are used to establish an index system for oil and gas pipeline risk evaluation on the basis of five important factors:corrosion,external interference,material/construction,natural disasters,and function and operation.Next,the index weight for oil and gas pipeline risk evaluation is computed by applying the improved AHP based on the five-scale method.Then,the TOPSIS of a multi-attribute decision-making theory is studied.The method for determining positive/negative ideal solutions and the normalized equation for benefit/cost indexes is improved to render TOPSIS applicable for the comprehensive risk evaluation of pipelines.The closeness coefficient of oil and gas pipelines is calculated by applying the improved TOPSIS.Finally,the weight and the closeness coefficient are combined to determine the risk level of pipelines.Empirical research using a long-distance pipeline as an example is conducted,and adjustment factors are used to verify the model.Results show that the risk evaluation model of oil and gas pipelines based on the improved AHP–TOPSIS is valuable and feasible.The model comprehensively considers the risk factors of oil and gas pipelines and provides comprehensive,rational,and scientific evaluation results.It represents a new decision-making method for systems engineering in pipeline enterprises and provides a comprehensive understanding of the safety status of oil and gas pipelines.The new system engineering decision-making method is important for preventing oil and gas pipeline accidents.
基金part of the Program of"Study on the mechanism of complex heat and mass transfer during batch transport process in products pipelines"funded under the National Natural Science Foundation of China(grant number 51474228)
文摘Oil product pipelines have features such as transporting multiple materials, ever-changing operating conditions, and synchronism between the oil input plan and the oil offloading plan. In this paper, an optimal model was established for a single-source multi-distribution oil pro- duct pipeline, and scheduling plans were made based on supply. In the model, time node constraints, oil offloading plan constraints, and migration of batch constraints were taken into consideration. The minimum deviation between the demanded oil volumes and the actual offloading volumes was chosen as the objective function, and a linear programming model was established on the basis of known time nodes＇ sequence. The ant colony optimization algo- rithm and simplex method were used to solve the model. The model was applied to a real pipeline and it performed well.
文摘This paper addresses the scheduling and inventory management of a straight pipeline system connecting a single refinery to multiple distribution centers.By increasing the number of batches and time periods,maintaining the model resolution by using linear programming-based methods and commercial solvers would be very time-consuming.In this paper,we make an attempt to utilize the problem structure and develop a decomposition-based algorithm capable of finding near-optimal solutions for large instances in a reasonable time.The algorithm starts with a relaxed version of the model and adds a family of cuts on the fly,so that a near-optimal solution is obtained within a few iterations.The idea behind the cut generation is based on the knowledge of the underlying problem structure.Computational experiments on a real-world data case and some randomly generated instances confirm the efficiency of the proposed algorithm in terms of the solution quality and time.
文摘s: Regarding the influencing factors in an optimal selection of pipeline design alternative as fuzzy variables with different weights, a fuzzy comprehensive assessment was applied to an optimal selection of the design alternative. Giving the Lanzhou-Chengdu pipeline as an example to explain the process, the result shows that this method is acceptable.
基金support of the NSERC Industrial Research Chair in Pipeline Transport Processes (held by RS Sanders)Canada’s Natural Sciences and Engineering Research Council (NSERC)the Industrial Sponsors (Canadian Natural Resources Limited, Fort Hills LLP, Nexen Inc., Saskatchewan Research Council Pipe Flow Technology CentreTM, Shell Canada Energy, Syncrude Canada Ltd., Total E&P Canada Ltd., Teck Resources Ltd. and Paterson & Cooke Consulting Engineers Ltd.)
文摘In water-lubricated pipeline transportation of heavy oil and bitumen, a thin oil film typically coats the pipe wall. A detailed study of the hydrodynamic effects of this fouling layer is critical to the design and operation of oil-water pipelines, as it can increase the pipeline pressure loss （and pumping power requirements） by 15 times or more. In this study, a parametric investigation of the hydrodynamic effects caused by the wall coating of viscous oil was conducted. A custom-built rectangular flow cell was used. A validated CFD-based procedure was used to determine the hydrodynamic roughness from the measured pressure losses. A similar procedure was followed for a set of pipe loop tests. The effects of the thickness of the oil coating layer, the oil viscosity, and water flow rate on the hydrodynamic roughness were evaluated. Oil viscosities from 3 to 21300 Pa s were tested. The results show that the equivalent hydrodynamic roughness produced by a wall coating layer of viscous oil is dependent on the coating thickness but essentially independent of oil viscosity. A new correlation was developed using these data to predict the hydrodynamic roughness for flow conditions in which a viscous oil coating is produced on the pipe wall.
基金The National Key Research and Development Program of China:Design and Key Technology Research of Non-metallic Flexible Risers for Deep Sea Mining(2022YFC2803701)The General Program of National Natural Science Foundation of China(52071336,52374022).
文摘Since leaks in high-pressure pipelines transporting crude oil can cause severe economic losses,a reliable leak risk assessment can assist in developing an effective pipeline maintenance plan and avoiding unexpected incidents.The fast and accurate leak detection methods are essential for maintaining pipeline safety in pipeline reliability engineering.Current oil pipeline leakage signals are insufficient for feature extraction,while the training time for traditional leakage prediction models is too long.A new leak detection method is proposed based on time-frequency features and the Genetic Algorithm-Levenberg Marquardt(GA-LM)classification model for predicting the leakage status of oil pipelines.The signal that has been processed is transformed to the time and frequency domain,allowing full expression of the original signal.The traditional Back Propagation(BP)neural network is optimized by the Genetic Algorithm(GA)and Levenberg Marquardt(LM)algorithms.The results show that the recognition effect of a combined feature parameter is superior to that of a single feature parameter.The Accuracy,Precision,Recall,and F1score of the GA-LM model is 95%,93.5%,96.7%,and 95.1%,respectively,which proves that the GA-LM model has a good predictive effect and excellent stability for positive and negative samples.The proposed GA-LM model can obviously reduce training time and improve recognition efficiency.In addition,considering that a large number of samples are required for model training,a wavelet threshold method is proposed to generate sample data with higher reliability.The research results can provide an effective theoretical and technical reference for the leakage risk assessment of the actual oil pipelines.
