Chevron U.s.a. Inc.

California Institute of Technology

The Texas A&M University System

Shuxing Cheng

Reynaldo Cardona

Zhao Zhang

Kellen Leigh Gunderson

Paul Duke

Konstantin Osypov

Adam Dean Halpert

Laura L Bandura

Larry A Bowden, Jr

Xin Feng

Grant Lee

Juan Esteban E Montero

Adam M Reeder

Steven R Szabo

Vishal Lall

Wayne Moses Burke

Hamsa Shwetha Venkataram

Chris Mattmann

Annie Didier

Asitang Mishra

Ziyu Jiang

Zhangyang Wang

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.

Systems and methods for identifying subsurface features as a function of position in a subsurface volume of interest

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.

Converting unstructured technical reports to structured technical reports using machine learning

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.

Systems and methods for identifying subsurface features as functions of feature positions in a subsurface volume of interest

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.

Designing plans using requirements knowledge graph

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.

Classification of character strings using machine-learning

Systems and methods for training a model that refines estimated parameter values include computer processors and non-transitory electronic storage that stores subsurface map data sets that correspond to different subsurface volumes of interest, the system configured to obtain training data including unrefined subsurface map data sets specifying estimated parameter values of a first parameter as a function of position within corresponding subsurface volumes of interest, obtain an initial seismic mapping model, generate a conditioned seismic mapping model, and store the conditioned seismic mapping model in the electronic storage.

Systems and methods for refining estimated parameter values in seismic imaging

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.

Classification of piping and instrumental diagram information using machine-learning

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Houston, TX, United States of America

Shuxing Cheng