公开(公告)号：US20190080164A1

公开(公告)日：2019-03-14

申请号：US16131946

申请日：2018-09-14

Applicant: CHEVRON U.S.A. INC.

Inventor： Paul Duke ,  Shuxing Cheng

IPC: G06K9/00 ,  G06N3/08 ,  G06N3/04 ,  G06K9/62

Abstract: Systems and methods for categorizing patterns of characters in a document by utilizing machine based learning techniques include generating character classification training data, building a character classification model based on the character classification training data; obtaining an image that includes a pattern of characters, the characters including one or more contours, applying the character classification model to the image to classify the contours, and applying the labels to clusters of the contours.

公开(公告)号：US11841479B2

公开(公告)日：2023-12-12

申请号：US16945517

申请日：2020-07-31

Applicant: CHEVRON U.S.A. INC. ,  THE TEXAS A&M UNIVERSITY SYSTEM

Inventor： Shuxing Cheng ,  Zhao Zhang ,  Kellen Leigh Gunderson ,  Reynaldo Cardona ,  Zhangyang Wang ,  Ziyu Jiang

IPC: G01V99/00 ,  G06F111/10 ,  G06F18/214

CPC classification number: G01V99/005 ,  G06F18/214 ,  G06F2111/10 ,  G06F2218/08

Abstract: Systems and methods are disclosed for identifying subsurface features as a function of position in a subsurface volume of interest. Exemplary implementations may include obtaining target subsurface data; obtaining a conditioned subsurface feature model; applying the conditioned subsurface feature model to the target subsurface data, which may include generating convoluted target subsurface data by convoluting the target subsurface data; generating target subsurface feature map layers by applying filters to the convoluted target subsurface data; detecting potential target subsurface features in the target subsurface feature map layers; masking the target subsurface features; and estimating target subsurface feature data by linking the masked subsurface features to the target subsurface feature data.

公开(公告)号：US11733424B2

公开(公告)日：2023-08-22

申请号：US16945486

申请日：2020-07-31

Applicant: CHEVRON U.S.A. INC.

Inventor： Shuxing Cheng ,  Zhao Zhang ,  Kellen Leigh Gunderson ,  Reynaldo Cardona

IPC: G06N20/00 ,  G01V99/00 ,  G06F30/27 ,  G06F113/08

CPC classification number: G01V99/005 ,  G06F30/27 ,  G06N20/00 ,  G06F2113/08

Abstract: Systems, devices, and methods are disclosed for identifying subsurface features as a function of position in a subsurface volume of interest. A computer-implemented method may include obtaining training subsurface data and corresponding training subsurface feature data; obtaining an initial subsurface feature model including tiers of elements; generating a conditioned subsurface feature model by training the initial subsurface feature model using the training subsurface data and the corresponding training subsurface feature data; and storing the conditioned subsurface feature model in the non-transient electronic storage.

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

IPC: G06F17/10 ,  G06N7/00 ,  G06F16/901 ,  G06N20/00 ,  G06F30/20 ,  G06F30/17

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.

公开(公告)号：US20200226325A1

公开(公告)日：2020-07-16

申请号：US16740066

申请日：2020-01-10

Applicant: Chevron U.S.A. Inc. ,  CALIFORNIA INSTITUTE OF TECHNOLOGY

Inventor： Asitang Mishra ,  Shuxing Cheng ,  Annie Didier ,  Chris Mattmann ,  Hamsa Shwetha Venkataram ,  Grant Lee ,  Wayne Moses Burke ,  Vishal Lall

IPC: G06F40/295 ,  G06N20/00 ,  G06F40/284 ,  G06F40/205

Abstract: A computer-implemented, machine learning-based method of converting an unstructured technical report into a structured technical report includes obtaining an unstructured technical report, tokenizing the unstructured technical report into an n-gram array, identifying and filtering non-interesting n-grams from the first n-gram array based on common language usage of the non-interesting n-grams and a determination that the non-interesting n-grams do not appear on a confirmed technical entity database, generating and displaying a technical entity candidate list from the filtered n-gram array, displaying, obtaining, from a pattern matching model and/or a graphical user interface, an indication that a technical entity candidate is a technical entity of interest, appending the technical entity of interest to the confirmed technical entity database, generating and displaying a structured technical report with the confirmed technical entities and corresponding technical entity value parameters, and iterating the process to refine the pattern matching model.

公开(公告)号：US20190236352A1

公开(公告)日：2019-08-01

申请号：US16376827

申请日：2019-04-05

Applicant: CHEVRON U.S.A. INC.

Inventor： Paul Duke ,  Shuxing Cheng

IPC: G06K9/00 ,  G06K9/62 ,  G06N3/08 ,  G06N3/04

CPC classification number: G06K9/00476 ,  G06K9/00456 ,  G06K9/6218 ,  G06K9/6256 ,  G06K9/6262 ,  G06K9/6271 ,  G06K2209/01 ,  G06N3/04 ,  G06N3/0454 ,  G06N3/08 ,  G06N5/003 ,  G06N20/10 ,  G06N20/20

Abstract: Systems and methods for identifying patterns of symbols in standardized system diagrams are disclosed. Disclosed implementations obtain or synthetically generate a symbol recognition training data set including multiple training images, generate a symbol recognition model based on the symbol recognition training data set, obtain an image comprising a pattern of symbols, group symbols into process loops based on the logical relationships captured by process loop identification algorithm, apply a character classification model to image contours to identify the characters and group characters into tags via hierarchical clustering, and store the identified tags, symbols and identified process loops in a relational database.