ARTIFICIAL INTELLIGENCE REAL-TIME MICROSEISM MONITORING NODE

    公开(公告)号:US20220182292A1

    公开(公告)日:2022-06-09

    申请号:US17507766

    申请日:2021-10-21

    Abstract: The application discloses an AI real-time microseism monitoring node, which includes a processor and a data acquisition device, an AI calculation device, and a communication device connected to the processor, wherein the AI calculation device is provided with pre-trained microseism data analysis Device, and the processor is configured to perform the following operations: controlling the data acquisition equipment to acquire microseism data; turning on the AI calculation device to calculate the acquired microseism data by means of the microseism data analysis device to determine the valid event data associated with the microseism; and sending the valid event data to the remote data center through the communication device.

    Real-time microseismic magnitude calculation method and device based on deep learning

    公开(公告)号:US11789173B1

    公开(公告)日:2023-10-17

    申请号:US18304026

    申请日:2023-04-20

    CPC classification number: G01V1/50 G06N3/047 G06N3/08

    Abstract: Embodiments of the present disclosure provide a real-time microseismic magnitude calculation method based on deep learning and a corresponding device. The method includes: constructing a DAS-based horizontal well microseismic monitoring system; constructing a training data set; constructing a magnitude calculation module, wherein the magnitude calculation module comprises two input branches of frequency spectrum and time waveform, the two input branches use a 3-layer convolution structure to extract frequency characteristic and waveform characteristic of a microseismic event, and then a model fusion is performed, and then 2 fully connected layers are used, and finally a calculated magnitude is outputted; training the magnitude calculation module; and analyzing and processing field data. The microseismic magnitude calculation method in the present disclosure improves the ability to quickly estimate the microseismic magnitude, without the need for converting the strain data, and improves the accuracy of the microseismic magnitude estimation.

    Real-Time Microseismic Magnitude Calculation Method and Device Based on Deep Learning

    公开(公告)号:US20230324577A1

    公开(公告)日:2023-10-12

    申请号:US18304026

    申请日:2023-04-20

    CPC classification number: G01V1/50 G06N3/047 G06N3/08

    Abstract: Embodiments of the present disclosure provide a real-time microseismic magnitude calculation method based on deep learning and a corresponding device. The method includes: constructing a DAS-based horizontal well microseismic monitoring system; constructing a training data set; constructing a magnitude calculation module, wherein the magnitude calculation module comprises two input branches of frequency spectrum and time waveform, the two input branches use a 3-layer convolution structure to extract frequency characteristic and waveform characteristic of a microseismic event, and then a model fusion is performed, and then 2 fully connected layers are used, and finally a calculated magnitude is outputted; training the magnitude calculation module; and analyzing and processing field data. The microseismic magnitude calculation method in the present disclosure improves the ability to quickly estimate the microseismic magnitude, without the need for converting the strain data, and improves the accuracy of the microseismic magnitude estimation.

    DAS same-well monitoring real-time microseismic effective event identification method based on deep learning

    公开(公告)号:US11899154B2

    公开(公告)日:2024-02-13

    申请号:US18324104

    申请日:2023-05-25

    CPC classification number: G01V1/48 G06N3/084

    Abstract: Embodiments of the present disclosure provide a DAS same-well monitoring real-time microseismic effective event identification method based on deep learning, including: constructing a DAS-based horizontal well microseismic monitoring system; constructing a training data set, including microseismic event data, pipe wave data and background noise data with different types of labels; constructing a signal identification module; training the signal identification module by using the training data set; preprocessing actual monitoring data, inputting the preprocessed data into the signal identification module to obtain an output result; marking microseismic events identified in the output result, and updating the marked microseismic events into the training data set; and adjusting and updating the signal identification module. The identification method according to the present disclosure can identify microseismic events in DAS same-well monitoring data in real time and efficiently.

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