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    Physics-Informed Sampling Scheme for Efficient Well Placement Optimization

    Source: Journal of Energy Resources Technology, Part B: Subsurface Energy and Carbon Capture:;2024:;volume( 001 ):;issue: 001::page 11005-1
    Author:
    Kim, Jongwook
    ,
    Kim, Dogyun
    ,
    Jo, Woosueng
    ,
    Kim, Joonyi
    ,
    Jo, Honggeun
    ,
    Choe, Jonggeun
    DOI: 10.1115/1.4066103
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Well placement optimization is a crucial task in terms of oil and gas recovery and economics in the field development plan. It poses significant challenges due to the multitude of local optima, which demand massive computational cost for global search algorithms. To address this, many proxy models have been applied for replacing reservoir simulations in many cases. Among these, convolutional neural network-based proxy models utilizing streamline time of flight maps as input demonstrated excellent performances. Nevertheless, these models exhibit diminishing performances during optimization processes, so additional retraining processes are required for successful results. In this study, we propose an initial sampling scheme using physics-informed quality maps incorporating static and dynamic information. The quality maps combine drainage area with permeability to represent the quality of each reservoir grid. The proposed scheme provides better performance than other sampling schemes. We demonstrate that the proposed scheme provides efficient well placement optimization regardless of the number of samples without retraining.
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      Physics-Informed Sampling Scheme for Efficient Well Placement Optimization

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4305479
    Collections
    • Journal of Energy Resources Technology, Part B: Subsurface Energy and Carbon Capture

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    contributor authorKim, Jongwook
    contributor authorKim, Dogyun
    contributor authorJo, Woosueng
    contributor authorKim, Joonyi
    contributor authorJo, Honggeun
    contributor authorChoe, Jonggeun
    date accessioned2025-04-21T10:05:28Z
    date available2025-04-21T10:05:28Z
    date copyright11/15/2024 12:00:00 AM
    date issued2024
    identifier issn2998-1638
    identifier otherjertb_1_1_011005.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4305479
    description abstractWell placement optimization is a crucial task in terms of oil and gas recovery and economics in the field development plan. It poses significant challenges due to the multitude of local optima, which demand massive computational cost for global search algorithms. To address this, many proxy models have been applied for replacing reservoir simulations in many cases. Among these, convolutional neural network-based proxy models utilizing streamline time of flight maps as input demonstrated excellent performances. Nevertheless, these models exhibit diminishing performances during optimization processes, so additional retraining processes are required for successful results. In this study, we propose an initial sampling scheme using physics-informed quality maps incorporating static and dynamic information. The quality maps combine drainage area with permeability to represent the quality of each reservoir grid. The proposed scheme provides better performance than other sampling schemes. We demonstrate that the proposed scheme provides efficient well placement optimization regardless of the number of samples without retraining.
    publisherThe American Society of Mechanical Engineers (ASME)
    titlePhysics-Informed Sampling Scheme for Efficient Well Placement Optimization
    typeJournal Paper
    journal volume1
    journal issue1
    journal titleJournal of Energy Resources Technology, Part B: Subsurface Energy and Carbon Capture
    identifier doi10.1115/1.4066103
    journal fristpage11005-1
    journal lastpage11005-15
    page15
    treeJournal of Energy Resources Technology, Part B: Subsurface Energy and Carbon Capture:;2024:;volume( 001 ):;issue: 001
    contenttypeFulltext
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