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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：US11320551B2
公开(公告)日：2022-05-03
申请号：US16685707
申请日：2019-11-15
发明人： Kuang-Hung Liu , Wei D. Liu , Huseyin Denli , Cody J. MacDonald
摘要： A method and apparatus for seismic interpretation including machine learning (ML). A method of training a ML system for seismic interpretation includes: preparing a collection of seismic images as training data; training an interpreter ML model to learn to interpret the training data, wherein: the interpreter ML model comprises a geologic objective function, and the learning is regularized by one or more geologic priors; and training a discriminator ML model to learn the one or more geologic priors from the training data. A method of hydrocarbon management includes: training the ML system for seismic interpretation; obtaining test data comprising a second collection of seismic images; applying the trained ML system to the test data to generate output; and managing hydrocarbons based on the output. A method includes performing an inference on test data with the interpreter and discriminator ML models to generate a feature probability map representative of subsurface features.
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公开(公告)号：US20200183032A1
公开(公告)日：2020-06-11
申请号：US16685707
申请日：2019-11-15
发明人： Kuang-Hung LIU , Wei D. Liu , Huseyin Denli , Cody J. Macdonald
摘要： A method and apparatus for seismic interpretation including machine learning (ML). A method of training a ML system for seismic interpretation includes: preparing a collection of seismic images as training data; training an interpreter ML model to learn to interpret the training data, wherein: the interpreter ML model comprises a geologic objective function, and the learning is regularized by one or more geologic priors; and training a discriminator ML model to learn the one or more geologic priors from the training data. A method of hydrocarbon management includes: training the ML system for seismic interpretation; obtaining test data comprising a second collection of seismic images; applying the trained ML system to the test data to generate output; and managing hydrocarbons based on the output. A method includes performing an inference on test data with the interpreter and discriminator ML models to generate a feature probability map representative of subsurface features.
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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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