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公开(公告)号：US11263209B2
公开(公告)日：2022-03-01
申请号：US16395189
申请日：2019-04-25
Applicant: Chevron U.S.A. Inc.
Inventor： Larry A. Bowden, Jr. , Esteban Montero
IPC: G06F16/33 , G06F16/242 , G06F16/2457 , G06F16/81
Abstract: Document information may define words, key groups of words, and sets of context words within a document. Word feature scores for words within the document may be generated. Key group feature scores for individual key groups of words may be generated based on aggregation of word feature scores the words within the individual key groups of words and word feature scores for words within corresponding sets of context words. A document feature score for the document may be generated based on aggregation of word feature scores for words within the document. The key group feature scores and the document feature score may enable context-sensitive searching of words/word vectors in the document.
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公开(公告)号：US20220317332A1
公开(公告)日：2022-10-06
申请号：US17219663
申请日：2021-03-31
Applicant: CHEVRON U.S.A. INC.
Inventor： Larry A. Bowden, Jr. , Lokendra Jain , Irina V. Prestwood
Abstract: Differential equations defining physics of a reservoir are modeled as a neural network. Measured data for the reservoir is used as boundary condition to calculate the different equation parameters. The result is a neural ordinary differential equation network that models reservoir characteristics (e.g., inter-well connectivities, response times for injection wells and production wells) using physics that are encoded into the network. The neural ordinary differential equation network provides a solution for the reservoir that is constrained by the physics of the reservoir.
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公开(公告)号：US12106192B2
公开(公告)日：2024-10-01
申请号：US17189004
申请日：2021-03-01
Applicant: CHEVRON U.S.A. INC.
Inventor： Mohamed Ibrahim Mohamed , Larry A. Bowden, Jr. , Xin Feng
IPC: G06N20/00 , G06F16/901 , G06F16/93
CPC classification number: G06N20/00 , G06F16/9024 , G06F16/93
Abstract: Multiple sets of documents for different domains may be used to train multiple domain-specific models. A graph model may be generated to include nodes representing concepts included within the domain-specific models. A white space not including any nodes within the graph model may be identified. Analysis of the white space may be performed based on two or more nodes at periphery of the white space. Words/documents that cover the white space may be generated. Novelty of concepts may be readily assessed using the graph model/white space.
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Patent Agency Ranking
公开(公告)号：US11921256B2
公开(公告)日：2024-03-05
申请号：US17219663
申请日：2021-03-31
Applicant: CHEVRON U.S.A. INC.
Inventor： Larry A. Bowden, Jr. , Lokendra Jain , Irina V. Prestwood
IPC: G01V99/00 , G06F17/13 , G06F30/27 , G06F30/28 , G06N3/02 , G06F111/04 , G06F113/08
CPC classification number: G01V99/005 , G06F17/13 , G06F30/27 , G06F30/28 , G06N3/02 , G06F2111/04 , G06F2113/08
Abstract: Differential equations defining physics of a reservoir are modeled as a neural network. Measured data for the reservoir is used as boundary condition to calculate the different equation parameters. The result is a neural ordinary differential equation network that models reservoir characteristics (e.g., inter-well connectivities, response times for injection wells and production wells) using physics that are encoded into the network. The neural ordinary differential equation network provides a solution for the reservoir that is constrained by the physics of the reservoir.
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公开(公告)号：US20220399085A1
公开(公告)日：2022-12-15
申请号：US17346463
申请日：2021-06-14
Applicant: Chevron U.S.A. Inc.
Inventor： Larry A. Bowden, Jr. , Dan Xie , Christopher Michael Lew , Joel Edward Schmidt
Abstract: A computer implemented method for designing chemical reactions for catalyst construction is described. The method includes extracting historical data including historic chemical reaction data and historic catalyst construction yield data and converting the historic chemical reaction data into graph models to represent molecular structure data. The method also includes incorporating the graph models into a chemical reaction algorithm and training a vectorized cognitive deep learning network of the chemical reaction algorithm by using the graph models and a property of the historic chemical reaction data to produce a catalyst chemical reaction model. Further, the method includes validating the catalyst chemical reaction model by inputting the historic chemical reaction data and comparing a generated property corresponding to the catalyst chemical reaction model to the property of the historic chemical reaction data. Lastly, the method includes updating the training of the catalyst chemical reaction model.
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公开(公告)号：US12112836B2
公开(公告)日：2024-10-08
申请号：US17346463
申请日：2021-06-14
Applicant: Chevron U.S.A. Inc.
Inventor： Larry A. Bowden, Jr. , Dan Xie , Christopher Michael Lew , Joel Edward Schmidt
Abstract: A computer implemented method for designing chemical reactions for catalyst construction is described. The method includes extracting historical data including historic chemical reaction data and historic catalyst construction yield data and converting the historic chemical reaction data into graph models to represent molecular structure data. The method also includes incorporating the graph models into a chemical reaction algorithm and training a vectorized cognitive deep learning network of the chemical reaction algorithm by using the graph models and a property of the historic chemical reaction data to produce a catalyst chemical reaction model. Further, the method includes validating the catalyst chemical reaction model by inputting the historic chemical reaction data and comparing a generated property corresponding to the catalyst chemical reaction model to the property of the historic chemical reaction data. Lastly, the method includes updating the training of the catalyst chemical reaction model.
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公开(公告)号：US12026185B2
公开(公告)日：2024-07-02
申请号：US17188774
申请日：2021-03-01
Applicant: Chevron U.S.A. Inc.
Inventor： Mohamed Ibrahim Mohamed , Larry A. Bowden, Jr. , Xin Feng
IPC: G06F16/332 , G06N5/02 , G06N20/00
CPC classification number: G06F16/3326 , G06N5/02 , G06N20/00
Abstract: Keywords obtained from a user and/or extracted from uploaded document(s) may be used to generate potential keywords. Documents may be identified based on the keywords and the potential keywords accepted by the user. A knowledge graph model representing the identified documents may be generated. The knowledge graph model may include document nodes representing the identified document and a search node representing the keywords. The relative position of the document nodes with respect to the search node may represent similarity between the corresponding documents and the keywords.
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公开(公告)号：US11436514B2
公开(公告)日：2022-09-06
申请号：US16837774
申请日：2020-04-01
Applicant: Chevron U.S.A. Inc.
Inventor： Larry A. Bowden, Jr. , Juan Esteban E. Montero , Shuxing Cheng , Adam M. Reeder , Xin Feng , Steven R. Szabo
Abstract: A requirements knowledge graph may include nodes for different elements of a requirement's relationship to physical characteristics, such as the requirement and the associated unsafe conditions, control objects, and hazards. A connection between nodes may define a relationship between the corresponding elements. The requirement, that is decomposed into a subject, an object, a predicate, and a context, may define a rule for the control object in the unsafe condition to avoid the hazard. A model for a plan (e.g., drilling plan for a well, design for a drilling tool) may be constructed based on the requirements knowledge graph. The plan may be generated based on the model.
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