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    Data-Driven Method for Predicting the Transportable Maximum Gas–Oil Ratio

    Source: Journal of Pipeline Systems Engineering and Practice:;2024:;Volume ( 015 ):;issue: 004::page 04024036-1
    Author:
    Dongyin Yan
    ,
    Haiyang Yu
    ,
    Yunlu Ma
    ,
    Yi Guo
    ,
    Zhuochao Li
    ,
    Fengyuan Yan
    ,
    Yongtu Liang
    DOI: 10.1061/JPSEA2.PSENG-1628
    Publisher: American Society of Civil Engineers
    Abstract: In the process of oil–gas mixing transportation, a too-high gas–oil ratio (GOR) will lead to instable flow pattern and high pipeline pressure, which has great safety risks. Therefore, it is important to determine the maximum GOR. At present, it relies mainly on commercial software to simulate the operation of the mixture transportation pipeline and the hydrothermal operating parameters to determine the maximum GOR under certain condition. This enumeration method is time-consuming and does not apply to continuously parameters. To solve this problem, a data-driven predictive model is developed. The new features are constructed by analyzing the factors influencing the transportable maximum gas–oil ratio (TMGOR), and the highly correlated features are selected from them as the new features. After analyzing the characteristics of the target variables, data mapping is performed, and the processed data set is fed into a neural network for training to obtain a data-driven predictive model of TMGOR. Finally, the validation is carried out with field data from an oilfield block in northwest China. The results showed that the average relative error of the model does not exceed 8.2% compared with the simulation results of commercial software, which has a high accuracy and can provide a rationale for the decision-making of mixed transfer in the field.
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      Data-Driven Method for Predicting the Transportable Maximum Gas–Oil Ratio

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    contributor authorDongyin Yan
    contributor authorHaiyang Yu
    contributor authorYunlu Ma
    contributor authorYi Guo
    contributor authorZhuochao Li
    contributor authorFengyuan Yan
    contributor authorYongtu Liang
    date accessioned2024-12-24T10:00:59Z
    date available2024-12-24T10:00:59Z
    date copyright11/1/2024 12:00:00 AM
    date issued2024
    identifier otherJPSEA2.PSENG-1628.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4298137
    description abstractIn the process of oil–gas mixing transportation, a too-high gas–oil ratio (GOR) will lead to instable flow pattern and high pipeline pressure, which has great safety risks. Therefore, it is important to determine the maximum GOR. At present, it relies mainly on commercial software to simulate the operation of the mixture transportation pipeline and the hydrothermal operating parameters to determine the maximum GOR under certain condition. This enumeration method is time-consuming and does not apply to continuously parameters. To solve this problem, a data-driven predictive model is developed. The new features are constructed by analyzing the factors influencing the transportable maximum gas–oil ratio (TMGOR), and the highly correlated features are selected from them as the new features. After analyzing the characteristics of the target variables, data mapping is performed, and the processed data set is fed into a neural network for training to obtain a data-driven predictive model of TMGOR. Finally, the validation is carried out with field data from an oilfield block in northwest China. The results showed that the average relative error of the model does not exceed 8.2% compared with the simulation results of commercial software, which has a high accuracy and can provide a rationale for the decision-making of mixed transfer in the field.
    publisherAmerican Society of Civil Engineers
    titleData-Driven Method for Predicting the Transportable Maximum Gas–Oil Ratio
    typeJournal Article
    journal volume15
    journal issue4
    journal titleJournal of Pipeline Systems Engineering and Practice
    identifier doi10.1061/JPSEA2.PSENG-1628
    journal fristpage04024036-1
    journal lastpage04024036-8
    page8
    treeJournal of Pipeline Systems Engineering and Practice:;2024:;Volume ( 015 ):;issue: 004
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
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