METHODS AND COMPUTING SYSTEMS FOR GEOSCIENCES AND PETRO-TECHNICAL COLLABORATION

    公开(公告)号:US20250028075A1

    公开(公告)日:2025-01-23

    申请号:US18908194

    申请日:2024-10-07

    Inventor: Shashi Menon

    Abstract: Computing systems and methods for geosciences collaboration are disclosed. In one embodiment, a method for geosciences collaboration includes obtaining a first set of geosciences information from a first computer system of the plurality of computer systems; distributing the first set of geosciences information from the first computer system to at least a second computer system; receiving a user input from the second computer system of the plurality of computer systems, the user input entered manually by a user; providing the user input to the first computer system; in response to providing the user input to the first computer system, receiving a revised set of geosciences information from the first computer system; and repeating the receiving a user input, the providing the user input, and the receiving the revised set of geosciences information until the revised set of geosciences information is determined to satisfy accuracy criteria.

    Building scalable geological property models using machine learning algorithms

    公开(公告)号:US12189075B2

    公开(公告)日:2025-01-07

    申请号:US17585441

    申请日:2019-12-03

    Abstract: A method of predicting rock properties at a selectable scale is provided, including receiving coordinates of locations of respective sample points, receiving measurement data associated with measurements or measurement interpretations for each sample point, receiving for each sample point a scale that indicates the scale used to obtain the measurements and/or measurement interpretations, wherein different scales are received for different sample points. A deep neural network (DNN) is trained by applying the received coordinates, measurement data, and scale associated with each sample point and associating the sample point with a rock property as a function of the coordinates, measurement data, and scale applied for the sample point. The DNN is configured to generate rock property data for a received request point having coordinates and a selectable scale, wherein the rock property data is determined for the request point as a function of the coordinates and the selectable scale.

    Coupling a simulator and at least one other simulator

    公开(公告)号:US12181623B2

    公开(公告)日:2024-12-31

    申请号:US16816333

    申请日:2020-03-12

    Abstract: Embodiments of methods, systems, and computer-readable media for coupling two or more simulators to simulate a coupled multi-physics model of a subsurface formation are provided. A coupling framework loads one or more simulators as shared libraries into a common process and a common memory space with a first simulator to create the coupled multi-physics model of the subsurface formation. During simulation, the coupling framework controls data exchange between the first simulator and the other simulator(s) through the common memory space and controls execution of the first simulator and the other simulator(s) responsive to the common process. In the event of two-way coupling, the coupling framework can receive feedback from the other simulator(s) and alter execution of the first simulator. In the event of grid misalignment, the coupling framework can map data between the first simulator and the other simulator(s) such as in a globally conservative (e.g., mass, energy, etc.) manner.

    Data analysis apparatus, data analysis method, and computer-readable recording medium

    公开(公告)号:US12169262B2

    公开(公告)日:2024-12-17

    申请号:US17617993

    申请日:2019-06-21

    Inventor: Chenhui Huang

    Abstract: A data analysis apparatus 10 includes; an align unit 11 that acquires a pair data of a first data indicating a characteristic of a specific region and a second data corresponding to the first data and indicating another characteristic of the specific region, and aligns the first data in order of their sizes, a classification model generation unit that groups the pair data based on a characteristic of an order distribution of the first data after alignment, classifies the pair data, and generates a classification model for classifying the pair data using the classification result, a regression model generation unit that performs machine learning for each group, using the first data constituting the pair data and the second data constituting the same pair data, and generates a regression model indicating a relation with the first data and the second data.

    Auto-generated transgressive systems tract maps

    公开(公告)号:US12164075B2

    公开(公告)日:2024-12-10

    申请号:US17622230

    申请日:2019-07-11

    Abstract: A computer-implemented method is provided for processing gross depositional environment (GDE) maps. The method includes receiving end-member lowstand systems tract (LST) and maximum flood surface (MFS) gross depositional environment (GDE) maps that represent a particular geographic area at different respective times spaced by a time interval, processing both of the LST and MFS GDE maps in accordance with a predefined set of mles that use geoprocessing operations to relate the content of both the LST and MFS GDE maps, and outputting a transgressive system tract (TST) map based on the processing.

    Landslide hazard monitoring and early warning method and system based on real 3D

    公开(公告)号:US12130401B1

    公开(公告)日:2024-10-29

    申请号:US18531737

    申请日:2023-12-07

    CPC classification number: G01V20/00 G08B21/10

    Abstract: A landslide risk monitoring and early warning method based on real 3D includes the following steps: S1, multi-source data fusion processing: collecting landslide-related data for an integrated fusion processing; S2, large-scale scene modeling, and analysis; S3, mesoscale scene modeling and analysis; S4, small-scale scene modeling and analysis; S5, landslide knowledge association and entity database construction; S6, landslide risk multi-scale dynamic assessment; S7, landslide multi-indicator monitoring and early warning. A landslide hazard monitoring and early warning system based on real 3D is also disclosed. The above-mentioned landslide risk monitoring and early warning method and system based on real 3D can realize different scale data acquisition and real 3D modeling of landslide geological disasters, establishment of full-factor real 3D landslide scene database, multi-scale spatial-temporal dynamic monitoring and analysis of landslide risk, and timely and intelligent multi-indicator early warning.

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