Method for determining favorable time window of infill well in unconventional oil and gas reservoir

    公开(公告)号:US11009620B2

    公开(公告)日:2021-05-18

    申请号:US16790801

    申请日:2020-02-14

    Abstract: A method for determining a favorable time window of an infill well of an unconventional oil and gas reservoir, which comprises the following steps: S1, establishing a three-dimensional geological model with physical properties and geomechanical parameters; S2, establishing a natural fracture network model in combination with indoor core-logging-seismic monitoring; S3, calculating complex fractures in hydraulic fracturing of parent wells; S4, establishing an unconventional oil and gas reservoir model and calculating a current pore pressure field; S5, establishing a dynamic geomechanical model and calculating a dynamic geostress field; S6, calculating complex fractures in horizontal fractures of the infill well in different production times of the parent wells based on pre-stage complex fractures and the current geostress field; S7, analyzing a microseismic event barrier region and its dynamic changes in infill well fracturing; and S8, analyzing the productivity in different infill times, and determining an infill time window.

    INTELLIGENT SAFETY SUPERVISION SYSTEM APPLIED TO SHIP

    公开(公告)号:US20250069378A1

    公开(公告)日:2025-02-27

    申请号:US18236859

    申请日:2023-08-22

    Abstract: An intelligent safety supervision system applied to a ship is provided. An image acquisition module is configured to acquires high-definition images in real time. An automatic recognition module is configured to obtains ship dynamic and static data. A ship server to-performs feature recognition on the ship dynamic and static data to obtain a data processing result, to-transmits the ship dynamic and static data and the data processing result, and receives alarm indication information. An alarm module outputs an alarm. A ship client displays the data processing result, and determines whether to transmit the alarm indication information according to the data processing result. A communication module receives and transmits the ship dynamic and static data and the data processing result. A shore-side supervision system includes a ship safety supervision big data analysis platform for performing secondary feature recognition on the ship dynamic and static data, so as to obtain a secondary data processing result.

    Grinding robot for inside wall of small diameter pipes

    公开(公告)号:US12186856B1

    公开(公告)日:2025-01-07

    申请号:US18628733

    申请日:2024-04-07

    Abstract: The present disclosure provides a grinding robot for an inside wall of a small diameter pipe. The grinding robot includes a grinding device, a transmission device, and a driving device. By arranging the grinding robot into the above three portions, the overall bending pipe passability of the robot can be increased, which is convenient for the grinding robot to grind the small diameter pipe. A first gimbal and two second gimbals provided in the transmission device enable the grinding robot to flexibly pass through bends of the pipe, and enable a grinding driving force to be variably transmitted to the grinding device. When the grinding body rotates and contacts a pipe wall, a reaction force of the pipe wall on the grinding body is balanced by an adjusting spring adjustment force in a balance adjusting device and a self-weight of a grinding body.

    Method of enhancing abnormal area of ground-penetrating radar image based on hybrid-supervised learning

    公开(公告)号:US12175633B1

    公开(公告)日:2024-12-24

    申请号:US18763894

    申请日:2024-07-03

    Abstract: A method of enhancing an abnormal area of a ground-penetrating radar image based on hybrid-supervised learning includes the steps of: building a database including a real image set, a simulation image set and a simulation image label set; adopting a generative adversarial network; processing semi-supervised training and unsupervised training alternately to obtain a trained model, then inputting a real radar image with abnormal area that needs to be enhanced into the model and processing through the generative network to output an abnormal-area-enhanced image. The method overcomes the problems of differences in characteristics between simulated images and real images, and low utilization efficiency of real image information by unsupervised methods, and improves the utilization efficiency of the enhanced network for real image information, the saliency of abnormal areas on real images, and the generalization ability of the enhanced network, therefore effectively enhances the significance of abnormal areas in ground-penetrating radar images.

    DOWNHOLE TRACTION SYSTEM
    57.
    发明公开

    公开(公告)号:US20240360733A1

    公开(公告)日:2024-10-31

    申请号:US18508923

    申请日:2023-11-14

    CPC classification number: E21B23/001 E21B47/06 E21B47/09 E21B47/13

    Abstract: 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

    公开(公告)号:US20240094419A1

    公开(公告)日:2024-03-21

    申请号:US18341781

    申请日:2023-06-27

    CPC classification number: G01V1/30 G01V2210/6169

    Abstract: 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.

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