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公开(公告)号：US11521122B2
公开(公告)日：2022-12-06
申请号：US16685692
申请日：2019-11-15
发明人： Wei D. Liu , Huseyin Denli , Kuang-Hung Liu , Michael H. Kovalski , Victoria M. Som De Cerff , Cody J. MacDonald , Diego A. Hernandez
摘要： A method and apparatus for automated seismic interpretation (ASI), including: obtaining trained models comprising a geologic scenario from a model repository, wherein the trained models comprise executable code; obtaining test data comprising geophysical data for a subsurface region; and performing an inference on the test data with the trained models to generate a feature probability map representative of subsurface features. A method and apparatus for machine learning, including: an ASI model; a training dataset comprising seismic images and a plurality of data portions; a plurality of memory locations, each comprising a replication of the ASI model and a different data portion of the training dataset; a plurality of data augmentation modules, each identified with one of the plurality of memory locations; a training module configured to receive output from the plurality of data augmentation modules; and a model repository configured to receive updated models from the training module.
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公开(公告)号：US20210247535A1
公开(公告)日：2021-08-12
申请号：US17248561
申请日：2021-01-29
摘要： A method for subsurface fault extraction using undirected graphs is provided. Extracting faults in the subsurface may assist in various stages of geophysical prospecting. To that end, an undirected graph may be used in order to identify distinctive fault branches in the subsurface. Fault probability data, from seismic data, may be used to establish connections in the undirected graph. Thereafter, some of the connections in the undirected graph may be removed based on analyzing one or more attributes, such as dip, azimuth, or context, associated with the connections or nodes associated with the connections. After which, the undirected graph may be analyzed in order to extract the faults in the subsurface.
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公开(公告)号：US11604298B2
公开(公告)日：2023-03-14
申请号：US17248561
申请日：2021-01-29
摘要： A method for subsurface fault extraction using undirected graphs is provided. Extracting faults in the subsurface may assist in various stages of geophysical prospecting. To that end, an undirected graph may be used in order to identify distinctive fault branches in the subsurface. Fault probability data, from seismic data, may be used to establish connections in the undirected graph. Thereafter, some of the connections in the undirected graph may be removed based on analyzing one or more attributes, such as dip, azimuth, or context, associated with the connections or nodes associated with the connections. After which, the undirected graph may be analyzed in order to extract the faults in the subsurface.
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公开(公告)号：US11119235B2
公开(公告)日：2021-09-14
申请号：US16059567
申请日：2018-08-09
摘要： A method to automatically interpret a subsurface feature within geophysical data, the method including: storing, in a computer memory, geophysical data obtained from a survey of a subsurface region; and extracting, with a computer, a feature probability volume by processing the geophysical data with one or more fully convolutional neural networks, which are trained to relate the geophysical data to at least one subsurface feature, wherein the extracting includes fusing together outputs of the one or more fully convolutional neural networks.
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5.发明申请公开(公告)号：US20200184374A1
公开(公告)日：2020-06-11
申请号：US16685692
申请日：2019-11-15
发明人： Wei D. LIU , Huseyin Denli , Kuang-Hung Liu , Michael H. Kovalski , Victoria M. Som De Cerff , Cody J. MacDonald , Diego A. Hernandez
摘要： A method and apparatus for automated seismic interpretation (ASI), including: obtaining trained models comprising a geologic scenario from a model repository, wherein the trained models comprise executable code; obtaining test data comprising geophysical data for a subsurface region; and performing an inference on the test data with the trained models to generate a feature probability map representative of subsurface features. A method and apparatus for machine learning, including: an ASI model; a training dataset comprising seismic images and a plurality of data portions; a plurality of memory locations, each comprising a replication of the ASI model and a different data portion of the training dataset; a plurality of data augmentation modules, each identified with one of the plurality of memory locations; a training module configured to receive output from the plurality of data augmentation modules; and a model repository configured to receive updated models from the training module.
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