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公开(公告)号：US11965995B2
公开(公告)日：2024-04-23
申请号：US18321746
申请日：2023-05-22
发明人： Yibo Wang , Zizhuo Ma , Yikang Zheng , Shaojiang Wu , Qingfeng Xue
CPC分类号： G01T1/2985 , G01N23/046 , G01N29/14 , G01N29/227 , A61B5/0095 , G01N21/1702 , G01N2021/1704 , G01N2223/1016 , G01N2223/108 , G01N2223/311 , G01N2223/419 , G01N2223/616
摘要： Embodiments of the present disclosure provide a multi-physical field imaging method based on PET-CT and DAS, comprising: wrapping distributed acoustic sensors on a surface of a non-metallic sample to be tested, and then placing them in a pressure device; loading triaxial pressures; preparing a tracer fluid; pumping the tracer fluid into the non-metallic sample; collecting PET images and CT images of internal structure of the non-metallic sample, meanwhile, monitoring internal acoustic emission events of the non-metallic sample in real time; combining the PET images with the CT images, to obtain PET/CT images; locating the acoustic emission events, and obtaining occurrence time and spatial location of internal structural perturbations; and analyzing a mechanism of fluid-solid coupling effect in the non-metallic sample under loaded stress. The imaging method and system of the present disclosure can accurately and reliably image the fluid-solid coupling process in the material.
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公开(公告)号：US20230296797A1
公开(公告)日：2023-09-21
申请号：US18321746
申请日：2023-05-22
发明人： Zizhuo Ma , Yibo Wang , Yikang Zheng , Shaojiang Wu , Qingfeng Xue
IPC分类号： G01N23/2206 , G01N29/14 , G01N29/22 , G01N23/046 , G01N23/2202
CPC分类号： G01N23/2206 , G01N23/046 , G01N23/2202 , G01N29/14 , G01N29/227
摘要： Embodiments of the present disclosure provide a multi-physical field imaging method based on PET-CT and DAS, comprising: wrapping distributed acoustic sensors on a surface of a non-metallic sample to be tested, and then placing them in a pressure device; loading triaxial pressures; preparing a tracer fluid; pumping the tracer fluid into the non-metallic sample; collecting PET images and CT images of internal structure of the non-metallic sample, meanwhile, monitoring internal acoustic emission events of the non-metallic sample in real time; combining the PET images with the CT images, to obtain PET/CT images; locating the acoustic emission events, and obtaining occurrence time and spatial location of internal structural perturbations; and analyzing a mechanism of fluid-solid coupling effect in the non-metallic sample under loaded stress. The imaging method and system of the present disclosure can accurately and reliably image the fluid-solid coupling process in the material.
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公开(公告)号：US11789173B1
公开(公告)日：2023-10-17
申请号：US18304026
申请日：2023-04-20
发明人： Shaojiang Wu , Yibo Wang , Yikang Zheng , Yi Yao
摘要： 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.
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公开(公告)号：US20230324577A1
公开(公告)日：2023-10-12
申请号：US18304026
申请日：2023-04-20
发明人： Shaojiang Wu , Yibo Wang , Yikang Zheng , Yi Yao
摘要： 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.
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公开(公告)号：US11899154B2
公开(公告)日：2024-02-13
申请号：US18324104
申请日：2023-05-25
发明人： Yikang Zheng , Yibo Wang , Shaojiang Wu , Yi Yao
摘要： 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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