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    GEOLOGICAL FEATURE DETECTION USING GENERATIVE ADVERSARIAL NEURAL NETWORKS
    11.
    发明申请
    GEOLOGICAL FEATURE DETECTION USING GENERATIVE ADVERSARIAL NEURAL NETWORKS 有权

    公开(公告)号：US20220351403A1

    公开(公告)日：2022-11-03

    申请号：US17621454

    申请日：2020-05-29

    Applicant: Landmark Graphics Corporation

    Inventor： Fan Jiang

    IPC: G06T7/70 ,  G06T3/40 ,  G06N3/04

    Abstract: Seismic image data acquired for a subsurface formation from a data acquisition system is input into a deep neural network to generate fault detection data for the subsurface formation comprising probability values at a grid of locations in the subsurface formation. The fault detection data is preprocessed via downsampling and distributed weighted factors and inputted into a generative adversarial network (GAN) upscaling generator to create high resolution fault detection data with minimized distortion and artifacts. The GAN upscaling generator is pre trained on synthetic fault data in a GAN training system using adversarial training against a GAN upscaling discriminator, and both the GAN upscaling generator and the GAN upscaling discriminator learn to approximate the distribution of the synthetic fault data.

    INFERRING SUBSURFACE KNOWLEDGE FROM SUBSURFACE INFORMATION
    13.
    发明公开
    INFERRING SUBSURFACE KNOWLEDGE FROM SUBSURFACE INFORMATION 审中-公开

    公开(公告)号：US20240069237A1

    公开(公告)日：2024-02-29

    申请号：US18305601

    申请日：2023-04-24

    Applicant: Landmark Graphics Corporation

    Inventor： Satyan Singh ,  Fan Jiang ,  Konstantin Osypov ,  Julianna Toms

    IPC: G01V1/48 ,  G01V1/46

    CPC classification number: G01V1/48 ,  G01V1/46

    Abstract: A geoscience knowledge system can be obtained, where the geoscience knowledge system can include one or more of publicly available information, industry information, proprietary information, or task specific information. The geoscience knowledge system can be represented as a graph, graph data, network nodes, image data, tokenized data, or textualized data. Subsurface information can be obtained such as from seismic images or other types of sensor data. The subsurface information can be transformed or pre-processed, such as denoising, to make it suitable for use by the geoscience knowledge system. Then subsurface knowledge can be inferred from the subsurface information using the geoscience knowledge system. The subsurface knowledge can provided estimates, approximations, or value of the subterranean formation of interest in order to calculate an economic model parameter, such as a hydrocarbon distribution proximate the subterranean formation of interest.

    Fault skeletonization for fault identification in a subterranean environment
    14.
    发明授权
    Fault skeletonization for fault identification in a subterranean environment 有权

    公开(公告)号：US11567225B2

    公开(公告)日：2023-01-31

    申请号：US16993525

    申请日：2020-08-14

    Applicant: Landmark Graphics Corporation

    Inventor： Fan Jiang ,  Phil Norlund

    IPC: G01V1/34 ,  G06V10/30 ,  E21B49/00 ,  G01V1/28 ,  G01V1/30 ,  G06K9/62 ,  G06T3/40

    Abstract: A system can receive fault likelihood data about a subterranean environment and apply a binary mask filter using a tuning parameter to convert the fault likelihood data to binary distribution data having a plurality of pixels arranged in a plurality of profiles in at least two directions. The system can perform, for each profile of the plurality of profiles, fault skeletonization on the binary distribution data to form fault skeletonization data with pixels connected that represent part of a fracture. The system can convert the fault skeletonization data to seismic volume data and combine and filter the seismic volume data in the at least two directions to form combined seismic volume data. The system can output the combined seismic volume data as an image for use in detecting objects to plan a wellbore operation.

    Facilitating hydrocarbon exploration and extraction by applying a machine-learning model to seismic data
    15.
    发明授权
    Facilitating hydrocarbon exploration and extraction by applying a machine-learning model to seismic data 有权

    公开(公告)号：US11428835B2

    公开(公告)日：2022-08-30

    申请号：US16827532

    申请日：2020-03-23

    Applicant: LANDMARK GRAPHICS CORPORATION

    Inventor： Zhili Wei ,  Meng Meng ,  Fan Jiang ,  Youli Mao ,  Phil Norlund

    IPC: G01V1/28 ,  G01V1/36 ,  G01V1/30 ,  G01V1/34 ,  G06N20/00 ,  G06N5/04

    Abstract: Hydrocarbon exploration and extraction can be facilitated using machine-learning models. For example, a system described herein can receive seismic data indicating locations of geological bodies in a target area of a subterranean formation. The system can provide the seismic data as input to a trained machine-learning model for determining whether the target area of the subterranean formation includes one or more types of geological bodies. The system can receive an output from the trained machine-learning model indicating whether or not the target area of the subterranean formation includes the one or more types of geological bodies. The system can then execute one or more processing operations for facilitating hydrocarbon exploration or extraction based on the seismic data and the output from the trained machine-learning model.

    DEEP LEARNING SEISMIC ATTRIBUTE FAULT PREDICTIONS
    16.
    发明申请
    DEEP LEARNING SEISMIC ATTRIBUTE FAULT PREDICTIONS 有权

    公开(公告)号：US20210181362A1

    公开(公告)日：2021-06-17

    申请号：US16745044

    申请日：2020-01-16

    Applicant: Landmark Graphics Corporation

    Inventor： Fan Jiang ,  Phil Norlund

    IPC: G01V1/28 ,  G01V1/00 ,  G06N3/08 ,  G06F17/18 ,  G06K9/62

    Abstract: This disclosure presents a fault prediction system using a deep learning neural network, such as a convolutional neural network. The fault prediction system utilizes as input seismic data, and then derives various seismic attributes from the seismic data. In various aspects, the seismic attributes can be normalized and have importance coefficients determined. A sub-set of seismic attributes can be selected to reduce computing resources and processing time. The deep learning neural network can utilize the seismic data and seismic attributes to determine parameterized results representing fault probabilities. The fault prediction system can utilize the fault probabilities to determine fault predictions which can be represented as a predicted new seismic data, such as using a three-dimensional image.

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