Journal of Pipeline Science and Engineering最新文献

{"title":"Data augmentation using conditional generative adversarial network (cGAN): Application for prediction of corrosion pit depth and testing using neural network","authors":"Haile Woldesellasse,&nbsp;Solomon Tesfamariam","doi":"10.1016/j.jpse.2022.100091","DOIUrl":"https://doi.org/10.1016/j.jpse.2022.100091","url":null,"abstract":"<div><p>Machine learning (ML) based algorithms, due to their ability to model nonlinear and complex relationship, have been used in predicting corrosion pit depth in oil and gas pipelines. Class imbalance and data scarcity are the challenging problems while training ML models. This paper utilized a conditional generative adversarial network (cGAN) to handle class imbalance problem in a corrosion dataset by generating new samples. Utility of the cGAN data augmentation is evaluated by training an artificial neural network (ANN) model. In addition, random oversampling and Borderline-SMOTE data generating techniques are used for comparison with cGAN. The testing accuracy of the ANN model increased greatly when trained by the cGAN based augmented dataset and this model performance improvement can be useful for a pipeline integrity management.</p></div>","PeriodicalId":100824,"journal":{"name":"Journal of Pipeline Science and Engineering","volume":"3 1","pages":"Article 100091"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49881023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction model of critical liquid‐carrying gas velocity for high gas‐to‐liquid ratio gathering pipelines","authors":"Rulong Ma ,&nbsp;Tingxia Ma ,&nbsp;Jiaying Kang ,&nbsp;Kang Yang ,&nbsp;Lianshun Li ,&nbsp;Lin Wang","doi":"10.1016/j.jpse.2022.100093","DOIUrl":"https://doi.org/10.1016/j.jpse.2022.100093","url":null,"abstract":"<div><p>As the pressure and temperature of natural gas pipelines decreases during operation, water and condensate accumulates form in the low areas of the pipelines, affecting the operational efficiency of the pipelines and even corroding them. The critical gas velocity is a key factor in predicting liquid loading onset in the pipeline, so that appropriate measures can be taken in advance and hazards can be reduced. This paper proposes a model for predicting pipeline liquid loading onset based on the liquid film and wall shear stress of zero, and applicable to different pipe diameters and different inclination angles. This model provides a more simplified and comprehensive prediction of pipeline fluid loading than other models with complex calculations. The critical gas velocity in this model is a function of the liquid holding rate rather than the liquid film thickness, and the critical gas velocity prediction in a phase-inclined pipe is carried out by an improved Belfroid angle correction term. The experimental data, field data and seven models in the published literature were compared and validated, and the errors were judged. The results showed that the new model outperformed the other models in terms of absolute mean error at full inclination angle, and was able to predict the pipeline liquid loading accurately.</p></div>","PeriodicalId":100824,"journal":{"name":"Journal of Pipeline Science and Engineering","volume":"3 1","pages":"Article 100093"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49881022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Plastic collapse analysis in multiaxially loaded defective pipe specimens at different temperatures","authors":"Y. Li,&nbsp;C. Sakonder,&nbsp;M. Paredes","doi":"10.1016/j.jpse.2022.100092","DOIUrl":"https://doi.org/10.1016/j.jpse.2022.100092","url":null,"abstract":"<div><p>A comprehensive numerical investigation is carried out using a newly developed constitutive model to describe failure at low temperatures in multiaxially loaded cracked pipes made of 316L stainless steel. The kinetic phase transformation and the temperature-dependent fracture criterion are implemented to accurately capture the mechanical response at different temperature levels. Although experimental observations of these simulations were not available, their results were quite consistent with some already published results obtained on similar materials and loading conditions at room temperature. The results indicate that the existing multiaxial plastic collapse failure criterion, including shearing, still provides a fail-safe design margin for low temperature loading conditions, including internal pressure. Moreover, martensite kinetic phase transformation plays an important role, especially during straining at low temperatures.</p></div>","PeriodicalId":100824,"journal":{"name":"Journal of Pipeline Science and Engineering","volume":"3 1","pages":"Article 100092"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49881024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A new calculation method and model of hydrate slurry flow of the multiphase pipeline in deep water gas field","authors":"Yan Gao ,&nbsp;Yue Xu ,&nbsp;Kunming Song ,&nbsp;Qingping Li ,&nbsp;Haiyuan Yao ,&nbsp;Haihong Chen ,&nbsp;Wuchang Wang ,&nbsp;Yuxing Li","doi":"10.1016/j.jpse.2022.100104","DOIUrl":"https://doi.org/10.1016/j.jpse.2022.100104","url":null,"abstract":"<div><p>The transportation environment of multiphase pipelines in deepwater gas fields is likely to cause the problem of hydrate flow safety. Aiming at the gas-liquid-solid three-phase flow problem containing hydrate, a hydrate slurry flow model for a multiphase pipeline in a deep-water gas field was developed based on the two-fluid model coupled with the population balance equation, and a mesh was carried out for the axial distance of pipeline and size difference of hydrate particles to obtain a transient solution. The multiphase flow characteristics of the pipeline are simulated upon engineering scale, the transient flow characteristics of gas and liquid phases, the aggregation and fragmentation characteristics of hydrate particles, and the coupling interaction between them were studied, which lays the foundation for further research on the characteristics of hydrate particle deposition and blockage based on particle size distribution. This study provides support for the safety of hydrate flow in multiphase pipelines in deepwater gas fields.</p></div>","PeriodicalId":100824,"journal":{"name":"Journal of Pipeline Science and Engineering","volume":"3 1","pages":"Article 100104"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49881589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Physics-informed Student’s t mixture regression model applied to predict mixed oil length","authors":"Ziyun Yuan ,&nbsp;Lei Chen ,&nbsp;Gang Liu ,&nbsp;Weiming Shao ,&nbsp;Yuhan Zhang ,&nbsp;Yunxiu Ma","doi":"10.1016/j.jpse.2022.100105","DOIUrl":"https://doi.org/10.1016/j.jpse.2022.100105","url":null,"abstract":"<div><p>Real-time estimation of thelength of mixed oil in a multi-product pipeline is a critical task during batch transportation. In previous studies, various predictive models have been built while they merely depended on a single predictive model to fulfill the regression work, and model performance severely deteriorated with the presence of outliers. The Student’s <em>t</em> mixture regression (SMR) model can identify multimode characteristics and reduce the impact of outliers. However, ignorance of physics knowledge and the simplistic assumption of a linear relationship between variables in the SMR may lead to unsatisfactory performance. In addition, the possible singularity problem can make the SMR fails to work. Motivated by resolving these issues, this paper proposes a physics-informed SMR modeling method by integrating the physics knowledge and the SMR to develop a robust hybrid predictive model for predicting the mixed oil length in a multi-product pipeline. Case studies are carried out on the measured dataset to demonstrate the effectiveness and advantages of the proposed new modeling method compared to the model entirely based on the SMR method and two state-of-the-art predictive models.</p></div>","PeriodicalId":100824,"journal":{"name":"Journal of Pipeline Science and Engineering","volume":"3 1","pages":"Article 100105"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49881021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Framework for Generating Pipeline Leak Datasets using PIPESIM","authors":"F. Idachaba, Olusegun Tomomewo","doi":"10.1016/j.jpse.2023.100113","DOIUrl":"https://doi.org/10.1016/j.jpse.2023.100113","url":null,"abstract":"","PeriodicalId":100824,"journal":{"name":"Journal of Pipeline Science and Engineering","volume":"152 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84201817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Iceberg-seabed interaction evaluation in clay seabed using tree-based machine learning algorithms","authors":"Hamed Azimi ,&nbsp;Hodjat Shiri ,&nbsp;Masoud Mahdianpari","doi":"10.1016/j.jpse.2022.100075","DOIUrl":"10.1016/j.jpse.2022.100075","url":null,"abstract":"<div><p>In Arctic offshore regions, the oil and gas hydrocarbons are transferred to the onshore basins through the subsea pipelines. However, the operational integrity of the subsea pipeline may be at risk of collision with traveling icebergs, which gouge the seabed in the Arctic shallow waters. Even though these sea bottom-founded structures are buried at a secure depth below the seafloor, the pipeline is still threatened by the ice scouring event extended much deeper than the ice tip due to the shear resistance of the seabed soil. Modeling the sub-gouge soil characteristics is a challenging problem that requires costly experimental and long-running finite element (FE) simulations. To overcome these challenges, in this paper, the reaction forces and sub-gouge soil deformations in clay were modeled using an advanced extra tree regression (ETR) algorithm, as a quick and cost-effective alternative for the early design phases of pipeline engineering projects. Eight ETR models, ETR 1 to ETR 8, were developed by using the input parameters governing the iceberg-seabed interaction problem. The collected data were randomly split into 70% for training the machine learning (ML) models and 30% for testing purposes. The most accurate ETR models and the most significant input parameters were identified by performing a sensitivity analysis. The comparison of the most accurate ETR models and decision tree regression (DTR), random forest regression (RFR), and gradient boosting regression (GBR) algorithms proved that the ETR models had better performance to simulate the ice keel seabed interaction in clay.</p></div>","PeriodicalId":100824,"journal":{"name":"Journal of Pipeline Science and Engineering","volume":"2 4","pages":"Article 100075"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667143322000476/pdfft?md5=d6a69ba337789792bf15a7b1d087c26d&pid=1-s2.0-S2667143322000476-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90212909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial neural network models of burst strength for thin-wall pipelines","authors":"Xian-Kui Zhu,&nbsp;William R. Johnson,&nbsp;Robert Sindelar,&nbsp;Bruce Wiersma","doi":"10.1016/j.jpse.2022.100090","DOIUrl":"10.1016/j.jpse.2022.100090","url":null,"abstract":"<div><p>Burst strength is critical to pipeline design, operation, and integrity management. The Barlow formula with the ultimate tensile stress (UTS) was often used to estimate burst strength of line pipes. To consider the plastic flow effect of pipeline steels, an average shear stress yield criterion was proposed, and the associated Zhu-Leis solution of burst strength was obtained for defect-free pipelines in term of UTS and strain hardening exponent, <em>n</em>, of materials. The Zhu-Leis solution was validated by more than 100 burst tests for various pipeline steels. The Zhu-Leis solution, when normalized by the Barlow strength, is a function of strain hardening rate, <em>n</em>, only. In contrast, experimental burst test data, when normalized by the Barlow strength, are a weak function of UTS and pipe diameter to thickness ratio <em>D</em>/<em>t</em>, in addition to be the function of <em>n</em>. Due to the difficulty of obtaining a closed-from solution using three-parameter regression, machine learning technology is adopted to develop alternative models of burst strength based on a large database of full-scale burst tests. In comparing to the regression, the machine learning method works well for both single and multiple parameters by introducing an artificial neural network (ANN), activation functions and learning algorithm for the network to learn from training data and then make predictions. Three ANN models were developed in this paper for predicting the burst strength of defect-free pipelines. Model 1 has one input variable and one hidden layer with three neurons; Model 2 has three input variables and one hidden layer with five neurons; and Model 3 has three input variables and two hidden layers with three neurons for the first hidden layer and two neurons for the second hidden layer. These three ANN models were then validated by the full-scale test data and evaluated through comparison with the Zhu-Leis solution and the linear regression results. On this basis, the best ANN model was recommended.</p></div>","PeriodicalId":100824,"journal":{"name":"Journal of Pipeline Science and Engineering","volume":"2 4","pages":"Article 100090"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667143322000622/pdfft?md5=11c58be76c0df0bbd93eb84625a6c0e6&pid=1-s2.0-S2667143322000622-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78841971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Experimental and numerical study on the flow characteristics of slug flow in a horizontal elbow","authors":"Xuewen Cao ,&nbsp;Pan Zhang ,&nbsp;Xiang Li ,&nbsp;Zheng Li ,&nbsp;Qianrong Zhang ,&nbsp;Jiang Bian","doi":"10.1016/j.jpse.2022.100076","DOIUrl":"10.1016/j.jpse.2022.100076","url":null,"abstract":"<div><p>Elbow is one of the most accident-prone locations in multiphase flow lines, and understanding the flow field is the key to manage the risk. In the meantime, the flow characteristics of slug flow are most complicated in the flow field. To clarify the flow characteristics of slug flow at the elbow, the influence and mechanism of the elbow on the flow characteristics of slug flow under different superficial gas-liquid velocities were studied with experiments and numerical simulations. A set of transparent loops with gas-liquid two phase flow was built. High-speed camera, pressure sensor, Doppler velocimeter and wire mesh sensor were used to collect flow pattern, pressure, slug velocity and gas-liquid distribution characteristics. In the meantime, the multi-fluid volume of fluid (VOF) model was used in the CFD simulations, and the simulation results were compared against the experimental results for verification. The results showed that the void fraction is highest at the inlet and lowest at the middle position, and the pressure and flow velocity at the extrados were higher than those at the intrados of the elbow. With the increase of the superficial liquid velocity and gas velocity, the number of bubbles accumulated inside the horizontal elbow increased, the slug flow liquid film velocity of the horizontal elbow increased, and the secondary flow phenomenon weakened.</p></div>","PeriodicalId":100824,"journal":{"name":"Journal of Pipeline Science and Engineering","volume":"2 4","pages":"Article 100076"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667143322000488/pdfft?md5=06db1547a369cdd3034bb21f6efd0ce3&pid=1-s2.0-S2667143322000488-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78215532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Review and analysis of pipeline leak detection methods","authors":"Naga Venkata Saidileep Korlapati ,&nbsp;Faisal Khan ,&nbsp;Quddus Noor ,&nbsp;Saadat Mirza ,&nbsp;Sreeram Vaddiraju","doi":"10.1016/j.jpse.2022.100074","DOIUrl":"10.1016/j.jpse.2022.100074","url":null,"abstract":"<div><p>A pipeline burst or rupture causing a leak may significantly impact the environment and the reputation of the company operating the pipeline. In recent years, oil and gas pipelines are expected to be equipped with leak detection systems for monitoring the operations and detecting the leaks. Although the leak detection methods used today may not prevent leaks from happening, they play a crucial role in limiting the impact of leak. There is a wide variety of leak detection methods developed and tested. This paper reviews these methods, analyze their advantages and limitations. It ends by highlighting the opportunities for future work to improve reliability and adaptability of leak detection methods in subsea region.</p></div>","PeriodicalId":100824,"journal":{"name":"Journal of Pipeline Science and Engineering","volume":"2 4","pages":"Article 100074"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667143322000464/pdfft?md5=29ea7bdb67f48380ff375ca757635d58&pid=1-s2.0-S2667143322000464-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89527159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}