Abstract: A method and system for real-time calculating a microseismic focal mechanism based on deep learning is provided, which belongs to the technical field of microseismic monitoring. The method includes: creating a training dataset, the training data including simulated DAS microseismic strain data and a focal mechanism corresponding to the simulated DAS microseismic strain data; training a focal mechanism calculation model by using the training dataset, with the simulated DAS microseismic strain data as an input and the focal mechanism corresponding to the simulated DAS microseismic strain data as a target output, so as to obtain a trained focal mechanism calculation model; collecting DAS microseismic strain data by a surface and downhole DAS acquisition system; performing preprocess operations such as removing abnormally large values on the DAS microseismic strain data; inputting the preprocessed DAS microseismic strain data into a trained focal mechanism calculation model to obtain a focal mechanism.

Abstract: 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.

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.

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.

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.

Abstract: 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.

Abstract: Embodiments of the present disclosure provide a DAS same-well monitoring real-time microseismic effective event identification method based on deep learning, comprising: constructing a DAS-based horizontal well microseismic monitoring system; constructing a training data set, comprising 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.