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110 条记录 (耗时 0.026 秒)

    DOWNHOLE TRACTION SYSTEM
    1.
    发明公开
    DOWNHOLE TRACTION SYSTEM 审中-公开

    公开(公告)号：US20240360733A1

    公开(公告)日：2024-10-31

    申请号：US18508923

    申请日：2023-11-14

    申请人： Chengdu University of Technology

    发明人： Jianguo ZHAO ,  Qingyou LIU ,  Haiyan ZHU ,  Min WAN ,  Guorong WANG ,  Xuecheng DONG ,  Xu LUO ,  Yingju PEI ,  Xingming WANG

    IPC分类号： E21B23/00 ,  E21B47/06 ,  E21B47/09 ,  E21B47/13

    CPC分类号： E21B23/001 ,  E21B47/06 ,  E21B47/09 ,  E21B47/13

    摘要： A downhole traction system includes a driving system and a downhole wheeled tractor. The driving system is connected with the downhole wheeled tractor; the downhole wheeled tractor comprises a tractor body, a power unit and a plurality of traction units; the plurality of traction units are arranged along the extension direction of the tractor body; each of the traction units comprises a driving arm, a supporting arm, a supporting wheel, a driving assembly and a supporting assembly; the driving arm and the supporting arm are movably connected with the tractor body; and the supporting wheel is connected with the driving arm and the supporting arm. When the supporting assembly drives the supporting arm to extend along the radial direction of the tractor body under the hydraulic drive action of the hydraulic power unit, the supporting wheel can be abutted against the well wall.

    SEISMIC QUANTITATIVE PREDICTION METHOD FOR SHALE TOC BASED ON SENSITIVE PARAMETER VOLUMES
    4.
    发明公开
    SEISMIC QUANTITATIVE PREDICTION METHOD FOR SHALE TOC BASED ON SENSITIVE PARAMETER VOLUMES 审中-公开

    公开(公告)号：US20240094419A1

    公开(公告)日：2024-03-21

    申请号：US18341781

    申请日：2023-06-27

    申请人： Chengdu University of Technology

    发明人： Chaorong Wu ,  Cheng Liu ,  Kaixing Huang ,  Yong Li ,  Yizhen Li ,  Junxiang Li ,  Yuexiang Hao

    IPC分类号： G01V1/30

    CPC分类号： G01V1/30 ,  G01V2210/6169

    摘要： A seismic quantitative prediction method for shale total organic carbon (TOC) based on sensitive parameter volumes is as follows. A target stratum for a TOC content to be measured is determined, logging curves with high correlations with TOC contents are analyzed, the logging curves are found as sensitive parameters; sample data are constructed using the sensitive parameters; a radial basis function (RBF) neural network is trained with the sample data as an input and the TOC content at a depth corresponding to the sample data as an output to obtain a RBF neural network prediction model; sensitive parameter volumes are obtained by using the sensitive parameters and post stack three-dimension seismic data to invert; prediction samples are constructed using the sensitive parameter volumes; the predicted samples are input to the RBF neural network prediction model to calculate corresponding TOC values, thereby the TOC content of the target stratum is predicted.

    Suspension modifier directly added into fracturing fluid for real-time proppant modification during fracturing and the application thereof
    6.
    发明授权
    Suspension modifier directly added into fracturing fluid for real-time proppant modification during fracturing and the application thereof 有权

    公开(公告)号：US11912933B1

    公开(公告)日：2024-02-27

    申请号：US18495788

    申请日：2023-10-27

    申请人： Chengdu University of Technology

    发明人： Bo Yang ,  Yu Liu ,  Hao Zhang ,  Di Yang ,  Min Ren ,  Yang Yang ,  Ying Zhong ,  Bin Yang ,  Jiping She

    IPC分类号： C09K8/60

    CPC分类号： C09K8/602

    摘要： The invention provides a suspension modifier directly added into fracturing fluid for real-time proppant modification during fracturing and the application thereof, relating to the field of oil and gas production technologies. The suspension modifier is a controlled release nanoemulsion and comprises surface hydrophobic modifier, surfactant, cosurfactant and water. The suspension modifier is directly added into clear-water or active-water fracturing fluid while the proppant is added into water. After stirring, the suspension modifier is capable of self-assembling and being adsorbed on the proppant surface, so that the proppant surface becomes hydrophobic and aerophilic. The invention no longer requires the proppant to be pretreated, and the bubble-suspended proppant can be obtained directly by adding the suspension modifier to the clear-water or active-water fracturing fluid, and meanwhile adding the proppant to the fracturing fluid. This technology is not only easy to operate, but also low in cost for proppant treatment.

    Method for Locating Abnormal Temperature Event of Distributed Optical Fiber
    9.
    发明公开
    Method for Locating Abnormal Temperature Event of Distributed Optical Fiber 审中-公开

    公开(公告)号：US20230324234A1

    公开(公告)日：2023-10-12

    申请号：US18192224

    申请日：2023-03-29

    申请人： Chengdu University of Technology

    发明人： Honghui WANG ,  Xiang WANG ,  Guangle YAO ,  Peng PENG ,  Jianbo YANG ,  Xianguo TUO

    IPC分类号： G01K11/32 ,  G06N3/08 ,  G06N3/0464

    CPC分类号： G01K11/32 ,  G06N3/08 ,  G06N3/0464

    摘要： A method of locating a temperature anomalies of a distributed optical fiber includes the steps of: (a) generating a training dataset having training samples; (b) setting labels for training samples; (c) building a convolutional neural network composed of multi-layer convolutional networks and a fully connected layer, training to form a convolutional neural network model; (d) utilizing a fiber-optic temperature sensing system for measurement of testing object; (e) sending acquired data into the convolutional neural network model to obtain output features, then processing mapping and binarization; (f) offsetting the binary feature to obtain an offset feature and calculating a cosine similarity; and (g) obtaining a location of the abnormal temperature event by identifying the offset feature with a largest cosine similarity and identifying its location in the sequence P.

    Density abrupt interface inversion method and system based on machine learning constraints
    10.
    发明授权
    Density abrupt interface inversion method and system based on machine learning constraints 有权

    公开(公告)号：US11768982B1

    公开(公告)日：2023-09-26

    申请号：US18116877

    申请日：2023-03-03

    申请人： Chengdu University of Technology

    发明人： Jun Li ,  Zhengwei Xu ,  Xuben Wang ,  Rui Wang ,  Shengxian Liang

    IPC分类号： G06F30/27 ,  G06F111/04

    CPC分类号： G06F30/27 ,  G06F2111/04

    摘要： Disclosed are a hybrid density abrupt interface inversion method based on machine learning constraints. The inversion method includes constructing an initial basin interface and randomly generating a disturbed basin interface data set; obtaining a basin interface data set through Hadamard product operation on the initial basin interface and the disturbed basin interface data set; obtaining a high-resolution density interface model data set through filling the basin interface data set with advanced functions; performing forward calculation to obtain a simulated gravity data set; carrying out mathematical transformation on the simulated gravity data set and weighting to obtain a low-resolution migration density interface model data set; optimizing a migration model-based deep learning network and mapping to obtain a high-resolution constrained density interface prior model; and constructing a stable nonlinear loss function and performing regularization inversion to obtain a high-resolution density interface model.