Patents by Inventor Wei D. LIU
Wei D. LIU has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 11521122Abstract: 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.Type: GrantFiled: November 15, 2019Date of Patent: December 6, 2022Assignee: ExxonMobil Upstream Research CompanyInventors: Wei D. Liu, Huseyin Denli, Kuang-Hung Liu, Michael H. Kovalski, Victoria M. Som De Cerff, Cody J. MacDonald, Diego A. Hernandez
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Patent number: 11397272Abstract: A method and apparatus for machine learning for use with automated seismic interpretation include: obtaining input data; extracting patches from a pre-extraction dataset based on the input data; transforming data of a pre-transformation dataset based on the input data and geologic domain knowledge and/or geophysical domain knowledge; and generating augmented data from the extracted patches and the transformed data. A method and apparatus for machine learning for use with automated seismic interpretation include: a data input module configured to obtain input data; a patch extraction module configured to extract patches from a pre-extraction dataset that is based on the input data; a data transformation module configured to transform data from a pre-transformation dataset that is based on the input data and geologic domain knowledge and/or geophysical domain knowledge; and a data augmentation module configured to augment data from the extracted patches and the transformed data.Type: GrantFiled: November 15, 2019Date of Patent: July 26, 2022Assignee: ExxonMobil Upstream Research CompanyInventors: Wei D. Liu, Huseyin Denli, Kuang-Hung Liu, Cody J. Macdonald
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Patent number: 11320551Abstract: 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.Type: GrantFiled: November 15, 2019Date of Patent: May 3, 2022Assignee: ExxonMobil Upstream Research CompanyInventors: Kuang-Hung Liu, Wei D. Liu, Huseyin Denli, Cody J. MacDonald
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Patent number: 11119235Abstract: 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.Type: GrantFiled: August 9, 2018Date of Patent: September 14, 2021Assignee: ExxonMobil Upstream Research CompanyInventors: Wei D. Liu, Diego A. Hernandez, Niranjan A. Subrahmanya, D. Braden Fitz-Gerald
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Patent number: 10915073Abstract: Systems and methods are provided for using a Deep Reinforcement Learning (DRL) agent to provide adaptive tuning of process controllers, such as Proportional-Integral-Derivative (PID) controllers. The agent can monitor process controller performance, and if unsatisfactory, can attempt to improve it by making incremental changes to the tuning parameters for the process controller. The effect of a tuning change can then be observed by the agent and used to update the agent's process controller tuning policy. It has been unexpectedly discovered that providing adaptive tuning based on incremental changes in tuning parameters, as opposed to making changes independent of current values of the tuning parameters, can provide enhanced or improved control over a controlled variable of a process.Type: GrantFiled: December 13, 2018Date of Patent: February 9, 2021Assignee: ExxonMobil Research and Engineering CompanyInventors: Thomas A. Badgwell, Kuang-Hung Liu, Niranjan A. Subrahmanya, Wei D. Liu, Michael H. Kovalski
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Publication number: 20200183035Abstract: A method and apparatus for machine learning for use with automated seismic interpretation include: obtaining input data; extracting patches from a pre-extraction dataset based on the input data; transforming data of a pre-transformation dataset based on the input data and geologic domain knowledge and/or geophysical domain knowledge; and generating augmented data from the extracted patches and the transformed data. A method and apparatus for machine learning for use with automated seismic interpretation include: a data input module configured to obtain input data; a patch extraction module configured to extract patches from a pre-extraction dataset that is based on the input data; a data transformation module configured to transform data from a pre-transformation dataset that is based on the input data and geologic domain knowledge and/or geophysical domain knowledge; and a data augmentation module configured to augment data from the extracted patches and the transformed data.Type: ApplicationFiled: November 15, 2019Publication date: June 11, 2020Inventors: Wei D. LIU, Huseyin DENLI, Kuang-Hung LIU, Cody J. MACDONALD
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Publication number: 20200183032Abstract: 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.Type: ApplicationFiled: November 15, 2019Publication date: June 11, 2020Inventors: Kuang-Hung LIU, Wei D. Liu, Huseyin Denli, Cody J. Macdonald
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Publication number: 20200184374Abstract: 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.Type: ApplicationFiled: November 15, 2019Publication date: June 11, 2020Inventors: Wei D. LIU, Huseyin Denli, Kuang-Hung Liu, Michael H. Kovalski, Victoria M. Som De Cerff, Cody J. MacDonald, Diego A. Hernandez
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Publication number: 20190278880Abstract: This disclosure generally relates to a methodology of effectively designing and/or discovering new materials based on microstructure, and more particularly, to designing and/or discovering new materials by combining material fundamentals and experimental data. The methodology disclosed herein provides cost-effective and time-effective solutions for material design that combine the benefits of both of the two major computational material design approaches: physics-based and data-driven computer models.Type: ApplicationFiled: March 7, 2019Publication date: September 12, 2019Inventors: Ning Ma, Niranjan A. Subrahmanya, Wei D. Liu, Sumathy Raman
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Publication number: 20190187631Abstract: Systems and methods are provided for using a Deep Reinforcement Learning (DRL) agent to provide adaptive tuning of process controllers, such as Proportional-Integral-Derivative (PID) controllers. The agent can monitor process controller performance, and if unsatisfactory, can attempt to improve it by making incremental changes to the tuning parameters for the process controller. The effect of a tuning change can then be observed by the agent and used to update the agent's process controller tuning policy. It has been unexpectedly discovered that providing adaptive tuning based on incremental changes in tuning parameters, as opposed to making changes independent of current values of the tuning parameters, can provide enhanced or improved control over a controlled variable of a process.Type: ApplicationFiled: December 13, 2018Publication date: June 20, 2019Inventors: Thomas A. Badgwell, Kuang-Hung Liu, Niranjan A. Subrahmanya, Wei D. Liu, Michael H. Kovalski
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Publication number: 20190064378Abstract: 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.Type: ApplicationFiled: August 9, 2018Publication date: February 28, 2019Inventors: Wei D. LIU, Diego A. Hernandez, Niranjan A. Subrahmanya, D. Braden Fitz-Gerald
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Patent number: 8957057Abstract: The present invention concerns the uses of an azaphilone compound of formula (I): formula (I): or a pharmaceutically acceptable derivative thereof as described in the specification for modulation of the activity of a nuclear hormone receptor and for prevention and/or treatment of a disease or disorder related to nuclear hormone receptor activity.Type: GrantFiled: April 5, 2010Date of Patent: February 17, 2015Assignee: Food Industry Research and Development InstituteInventors: Ta-Wei D. Liu, Yen-Lin Chen, Ming-Der Wu, Ming-Jen Cheng, Hui-Ping Chen, Wen-Jung Wu, Kai-Ping Chen, Yu-Shan Lin, Gwo-Fang Yuan
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Publication number: 20100256227Abstract: The present invention concerns the uses of an azaphilone compound of formula (I): formula (I): or a pharmaceutically acceptable derivative thereof as described in the specification for modulation of the activity of a nuclear hormone receptor and for prevention and/or treatment of a disease or disorder related to nuclear hormone receptor activity.Type: ApplicationFiled: April 5, 2010Publication date: October 7, 2010Inventors: Ta-Wei D. LIU, Yen-Lin Chen, Ming-Der Wu, Ming-Jen Cheng, Hui-Ping Chen, Wen-Jung Wu, Kai-Ping Chen, Yu-Shan Lin, Gwo-Fang Yuan