中科微点-全球专利检索

  1. Login
  2. Sign Up

Search Results

17 records (took 0.014 seconds)

    Geological feature detection using generative adversarial neural networks
    1.
    发明授权
    Geological feature detection using generative adversarial neural networks 有权

    公开(公告)号：US12266132B2

    公开(公告)日：2025-04-01

    申请号：US17621454

    申请日：2020-05-29

    Applicant: Landmark Graphics Corporation

    Inventor： Fan Jiang

    IPC: G06T7/70 ,  G01V1/30 ,  G06N3/045 ,  G06N3/047 ,  G06N3/08 ,  G06T3/40

    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.

    FREQUENCY-DEPENDENT MACHINE LEARNING MODEL IN SEISMIC INTERPRETATION
    3.
    发明公开
    FREQUENCY-DEPENDENT MACHINE LEARNING MODEL IN SEISMIC INTERPRETATION 审中-公开

    公开(公告)号：US20230288594A1

    公开(公告)日：2023-09-14

    申请号：US17825914

    申请日：2022-05-26

    Applicant: Landmark Graphics Corporation

    Inventor： Fan Jiang ,  Alejandro Jaramillo ,  Steven Roy Angelovich

    IPC: G01V1/34 ,  G01V1/28 ,  G01V1/30 ,  G06N20/20

    CPC classification number: G01V1/345 ,  G01V1/282 ,  G01V1/301 ,  G06N20/20 ,  G01V2210/642

    Abstract: Frequency-dependent machine-learning (ML) models can be used to interpret seismic data. A system can apply spectral decomposition to pre-processed training data to generate frequency-dependent training data of two or more frequencies. The system can train two or more ML models using the frequency-dependent training data. Subsequent to training the two or more ML models, the system can apply the two or more ML models to seismic data to generate two or more subterranean feature probability maps. The system can perform an analysis of aleatoric uncertainty on the two or more subterranean feature probability maps to create an uncertainty map for aleatoric uncertainty. Additionally, the system can generate a filtered subterranean feature probability map based on the uncertainty map for aleatoric uncertainty.

    MACHINE-LEARNING BASED GEOBODY PREDICTION WITH SPARSE INPUT
    4.
    发明申请
    MACHINE-LEARNING BASED GEOBODY PREDICTION WITH SPARSE INPUT 有权

    公开(公告)号：US20250044468A1

    公开(公告)日：2025-02-06

    申请号：US18362402

    申请日：2023-07-31

    Applicant: Landmark Graphics Corporation

    Inventor： Marc Paul Servais ,  Graham Baines ,  Daniel Thomas-Possee ,  Fan Jiang ,  Konstantin Osypov

    IPC: G01V1/28 ,  G01V1/30 ,  G01V1/34 ,  G06N3/08

    Abstract: Some implementations may include a method for detecting, by a learning machine, a geobody in a seismic volume. The method may include receiving a first seismic input tile representing first seismic data from the seismic volume; receiving a first guide input tile including first labels that indicate presence of the geobody in a respective region in the seismic volume or absence of the geobody in the respective region, and one or more unlabeled regions that make no indication about presence or absence of the geobody; and determining, based on the first seismic input tile and the first guide input tile, a first prediction about geobody presence or absence in the seismic volume.

    DEEP LEARNING MODEL WITH DILATION MODULE FOR FAULT CHARACTERIZATION
    7.
    发明申请
    DEEP LEARNING MODEL WITH DILATION MODULE FOR FAULT CHARACTERIZATION 有权

    公开(公告)号：US20220413173A1

    公开(公告)日：2022-12-29

    申请号：US17359435

    申请日：2021-06-25

    Applicant: Landmark Graphics Corporation

    Inventor： Fan Jiang ,  Philip Norlund

    IPC: G01V1/30 ,  G01V1/18 ,  G06N3/08 ,  G06N3/04 ,  E21B49/00

    Abstract: A system can receive seismic data that can correlate to a subterranean formation. The system can derive a set of seismic attributes from the seismic data. The seismic attributes can include discontinuity-along-dip. The system can determine parameterized results by analyzing the seismic data and the seismic attributes using a deep learning neural network. The deep learning neural network can include a dilation module. The system can determine one or more fault probabilities of the subterranean formation using the parameterized results. The system can output the fault probabilities for use in a hydrocarbon exploration operation.

    METHODS AND SYSTEMS FOR SEISMIC MODELING USING MULTIPLE SEISMIC SOURCE TYPES
    8.
    发明申请
    METHODS AND SYSTEMS FOR SEISMIC MODELING USING MULTIPLE SEISMIC SOURCE TYPES 审中-公开
    使用多种地震源类型进行地震建模的方法与系统

    公开(公告)号：US20150346385A1

    公开(公告)日：2015-12-03

    申请号：US14649192

    申请日：2013-12-10

    Applicant: LANDMARK GRAPHICS CORPORATION

    Inventor： Shengwen Jin ,  Fan Jiang ,  Yiqing Ren

    IPC: G01V99/00

    CPC classification number: G01V99/005 ,  G01V11/00 ,  G01V99/00 ,  G01V2210/645 ,  G01V2210/66 ,  G01V2210/67

    Abstract: Seismic Modeling Using Multiple Seismic Sources. At least some of the illustrative embodiments are methods including: simulating an effect of multiple seismic sources concurrently on a geologic formation, the simulating by: reading data from a seismic source file, wherein the seismic source file describes at least two seismic source types; modeling, by a computer system, the concurrent propagation of acoustic energy from the at least two seismic sources types through the geologic formation; and generating a seismic output file.

    Abstract translation: 使用多个地震源的地震建模。 示例性实施例中的至少一些是包括：通过以下步骤模拟多个地震源的多个地震源的影响：模拟地震源文件中的数据，其中所述地震源文件描述至少两个地震源类型; 通过计算机系统建模，通过地质构造从至少两个地震源类型同时传播声能; 并产生地震输出文件。

    LEARNING HYDROCARBON DISTRIBUTION FROM SEISMIC IMAGE
    10.
    发明公开
    LEARNING HYDROCARBON DISTRIBUTION FROM SEISMIC IMAGE 审中-公开

    公开(公告)号：US20240069228A1

    公开(公告)日：2024-02-29

    申请号：US17896748

    申请日：2022-08-26

    Applicant: Landmark Graphics Corporation

    Inventor： Konstantin Osypov ,  Fan Jiang ,  Marcelo Gomes ,  Satyan Singh

    IPC: G01V1/30 ,  E21B49/00 ,  G01V1/34

    CPC classification number: G01V1/30 ,  E21B49/00 ,  G01V1/345 ,  G01V2210/60 ,  G01V2210/74

    Abstract: The disclosure relates to determining rock properties of subterranean formations and learning the distribution of hydrocarbons in the formations. A geometrical element spread function is disclosed that quantifies distortion of the geology as seen by the geophysicists who process seismic images of the subterranean formations. A method of determining the rock properties using the seismic images and synthetic images is provided. In one example, the method includes: (1) obtaining seismic data from a subterranean formation using a seismic acquisition system, (2) generating one or more seismic images of the subterranean formation using the seismic data, (3) creating one or more synthetic images from the one or more seismic images, and (4) determining rock properties of the subterranean formation based on the one or more seismic images and the one or more synthetic images.

Patent Agency Ranking