中科微点-全球专利检索

  1. Login
  2. Sign Up

Search Results

10 records (took 0.034 seconds)

    RESERVOIR FLUID PROPERTY MODELING USING MACHINE LEARNING
    1.
    发明申请
    RESERVOIR FLUID PROPERTY MODELING USING MACHINE LEARNING 有权

    公开(公告)号：US20220307357A1

    公开(公告)日：2022-09-29

    申请号：US17293454

    申请日：2020-06-12

    Applicant: LANDMARK GRAPHICS CORPORATION

    Inventor： Ajay Pratap Singh ,  Suryansh Purwar ,  Ashwani Dev ,  Satyam Priyadarshy

    IPC: E21B43/16 ,  E21B47/06 ,  E21B47/003

    Abstract: System and methods for tuning equation of state (EOS) characterizations are presented. Pressure-volume-temperature (PVT) data is obtained for downhole fluids within a reservoir formation. A component grouping for an EOS model of the downhole fluids is determined, based on the obtained PVT data. The component grouping is used to estimate properties of the downhole fluids for a current stage of a downhole operation within the formation. A machine learning model is trained to minimize an error between the estimated properties and actual fluid properties measured during the current stage of the operation, where the component grouping for the EOS model is iteratively adjusted by the machine learning model until the error is minimized. The EOS model is tuned using the adjusted component grouping. Fluid properties are estimated for one or more subsequent stages of the downhole operation to be performed along the wellbore, based on the tuned EOS model.

    DRILL BIT REPAIR TYPE PREDICTION USING MACHINE LEARNING
    2.
    发明申请
    DRILL BIT REPAIR TYPE PREDICTION USING MACHINE LEARNING 审中-公开

    公开(公告)号：US20200149354A1

    公开(公告)日：2020-05-14

    申请号：US16611817

    申请日：2018-08-31

    Applicant: LANDMARK GRAPHICS CORPORATION

    Inventor： Ajay Pratap Singh ,  Roxana Nielsen, Jr. ,  Satyam Priyadarshy ,  Ashwani Dev ,  Geetha Gopakumar Nair ,  Suresh Venugopal

    IPC: E21B12/02 ,  G06N3/08 ,  E21B10/62

    Abstract: The subject disclosure provides for a mechanism implemented with neural networks through machine learning to predict wear and relative performance metrics for performing repairs on drill bits in a next repair cycle, which can improve decision making by drill bit repair model engines, drill bit design, and help reduce the cost of drill bit repairs. The machine learning mechanism includes obtaining drill bit data from different data sources and integrating the drill bit data from each of the data sources into an integrated dataset. The integrated dataset is pre-processed to filter out outliers. The filtered dataset is applied to a neural network to build a machine learning based model and extract features that indicate significant parameters affecting wear. A repair type prediction is determined with the applied machine learning based model and is provided as a signal for facilitating a drill bit operation on a cutter of the drill bit.

    History matching of hydrocarbon production from heterogenous reservoirs
    4.
    发明授权
    History matching of hydrocarbon production from heterogenous reservoirs 有权

    公开(公告)号：US10909281B2

    公开(公告)日：2021-02-02

    申请号：US15765961

    申请日：2015-12-18

    Applicant: LANDMARK GRAPHICS CORPORATION

    Inventor： Ajay Pratap Singh ,  Hasnain A. Khan

    IPC: G06F30/20 ,  E21B43/00 ,  E21B41/00 ,  G06F111/06 ,  G06F111/10

    Abstract: A hydrocarbon field including thief zones can be modeled based on production history data and supplemental data constraining a thief-zone distribution across the field. In various embodiments, a numerical optimization approach involves creating a plurality of model thief-zone distributions with varying parameter values, parameterizing permeability maps obtained for the thief-zone distributions and aggregating across the thief-zone distributions to obtain distributions of coefficients associated with the parameterization, and then iteratively sampling the coefficient distributions, determining a permeability map of the model based on the selected coefficients, and measuring a deviation between the measured production history data and simulated production history data derived from the computational model until a match is achieved.

    HISTORY MATCHING OF HYDROCARBON PRODUCTION FROM HETEROGENOUS RESERVOIRS
    7.
    发明申请
    HISTORY MATCHING OF HYDROCARBON PRODUCTION FROM HETEROGENOUS RESERVOIRS 审中-公开

    公开(公告)号：US20190065640A1

    公开(公告)日：2019-02-28

    申请号：US15765961

    申请日：2015-12-18

    Applicant: LANDMARK GRAPHICS CORPORATION

    Inventor： Ajay Pratap Singh ,  Hasnain A. Khan

    IPC: G06F17/50 ,  E21B41/00

    Abstract: A hydrocarbon field including thief zones can be modeled based on production history data and supplemental data constraining a thief-zone distribution across the field. In various embodiments, a numerical optimization approach involves creating a plurality of model thief-zone distributions with varying parameter values, parameterizing permeability maps obtained for the thief-zone distributions and aggregating across the thief-zone distributions to obtain distributions of coefficients associated with the parameterization, and then iteratively sampling the coefficient distributions, determining a permeability map of the model based on the selected coefficients, and measuring a deviation between the measured production history data and simulated production history data derived from the computational model until a match is achieved.

    Forecasting Production Data for Existing Wells and New Wells
    8.
    发明申请
    Forecasting Production Data for Existing Wells and New Wells 审中-公开
    预测现有井和新井的生产数据

    公开(公告)号：US20160260181A1

    公开(公告)日：2016-09-08

    申请号：US14646688

    申请日：2014-04-30

    Applicant: LANDMARK GRAPHICS CORPORATION

    Inventor： Ajay Pratap Singh

    IPC: G06Q50/02 ,  G06Q10/04 ,  G06Q10/06

    CPC classification number: G06Q50/02 ,  E21B41/00 ,  E21B49/00 ,  G06Q10/04 ,  G06Q10/0639 ,  G06Q10/067

    Abstract: Systems and methods for forecasting production data for existing wells and new wells using normalized production data for the existing wells, clustering of the existing wells, a production data matrix for each cluster of existing wells, a fitted decline curve for each cluster of existing wells based on a respective production data matrix, and a standard decline curve.

    Abstract translation: 使用现有井的标准化生产数据，现有井的聚类，现有井的每个集群的生产数据矩阵，现有井的每个集群的拟合下降曲线来预测现有井和新井的生产数据的系统和方法 在各自的生产数据矩阵和标准下降曲线上。

Patent Agency Ranking