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    Machine Learning–Driven Fracture Analysis and Prediction in Tight Sandstone Gas Reservoirs Using Geophysical Well Logging

    Source: Journal of Energy Engineering:;2026:;Volume ( 152 ):;issue: 004::page 04026030-1
    Author:
    Cui, Yuehua
    ,
    Gao, Jianwen
    ,
    Song, Shijun
    ,
    Luo, Wenqin
    ,
    Chen, Sisi
    ,
    Wang, Lei
    ,
    Fu, Kun
    ,
    Tang, Tianrui
    ,
    Wang, Jiahao
    ,
    Yu, Hongyan
    DOI: 10.1061/JLEED9.EYENG-6541
    Publisher: American Society of Civil Engineers
    Abstract: AbstractAccurate fracture characterization is fundamental for optimizing hydrocarbon recovery in tight sandstone reservoirs, yet traditional methods like borehole image logging remain cost-prohibitive for field-scale deployment. This study develops a ...
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      Machine Learning–Driven Fracture Analysis and Prediction in Tight Sandstone Gas Reservoirs Using Geophysical Well Logging

    URI
    https://yetl.yabesh.ir/yetl1/handle/yetl/4312046
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    • Journal of Energy Engineering

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    contributor authorCui, Yuehua
    contributor authorGao, Jianwen
    contributor authorSong, Shijun
    contributor authorLuo, Wenqin
    contributor authorChen, Sisi
    contributor authorWang, Lei
    contributor authorFu, Kun
    contributor authorTang, Tianrui
    contributor authorWang, Jiahao
    contributor authorYu, Hongyan
    date accessioned2026-08-20T11:20:04Z
    date available2026-08-20T11:20:04Z
    date copyright2026/05/17
    date issued2026
    identifier otherJLEED9.EYENG-6541.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4312046
    description abstractAbstractAccurate fracture characterization is fundamental for optimizing hydrocarbon recovery in tight sandstone reservoirs, yet traditional methods like borehole image logging remain cost-prohibitive for field-scale deployment. This study develops a ...
    publisherAmerican Society of Civil Engineers
    titleMachine Learning–Driven Fracture Analysis and Prediction in Tight Sandstone Gas Reservoirs Using Geophysical Well Logging
    typeJournal Article
    journal volume152
    journal issue4
    journal titleJournal of Energy Engineering
    identifier doi10.1061/JLEED9.EYENG-6541
    journal fristpage04026030-1
    journal lastpage04026030-14
    page14
    treeJournal of Energy Engineering:;2026:;Volume ( 152 ):;issue: 004
    contenttypeFulltext
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