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    STATIC EARTH MODEL CALIBRATION METHODS AND SYSTEMS USING TORTUOSITY EVALUATIONS
    2.
    发明申请
    STATIC EARTH MODEL CALIBRATION METHODS AND SYSTEMS USING TORTUOSITY EVALUATIONS 审中-公开
    静态地球模型校准方法和系统使用质量评估

    公开(公告)号：US20160209546A1

    公开(公告)日：2016-07-21

    申请号：US14913985

    申请日：2013-08-29

    Applicant: LANDMARK GRAPHICS CORPORATION

    Inventor： Travis St. George Ramsay ,  Dominic Camilleri

    IPC: G01V99/00 ,  G05B17/02

    CPC classification number: G01V99/005 ,  G05B17/02

    Abstract: At least some of the disclosed systems and methods obtain a static earth model having a three-dimensional grid with multiple cells. Further, at least some of the disclosed systems and methods determine a plurality of geobodies for the static earth model, each geobody comprising a plurality of connected cells. Further, at least some of the disclosed systems and methods compute one or more tortuosity values for at least one of the plurality of geobodies. Further, at least some of the disclosed systems and methods calibrate the static earth model based on the one or more computed tortuosity values. Further, at least some of the disclosed systems and methods use the calibrated static earth model as input to a flow simulator.

    Abstract translation: 所公开的系统和方法中的至少一些获得具有具有多个单元的三维网格的静态地球模型。 此外，所公开的系统和方法中的至少一些确定用于静态地球模型的多个地球体，每个地球体包括多个连接的小区。 此外，所公开的系统和方法中的至少一些计算多个地球体中的至少一个的一个或多个曲折度值。 此外，所公开的系统和方法中的至少一些基于一个或多个计算的曲折度值来校准静态地球模型。 此外，所公开的系统和方法中的至少一些使用校准的静态地球模型作为流动模拟器的输入。

    PETROLEUM RESERVOIR BEHAVIOR PREDICTION USING A PROXY FLOW MODEL
    4.
    发明申请
    PETROLEUM RESERVOIR BEHAVIOR PREDICTION USING A PROXY FLOW MODEL 有权

    公开(公告)号：US20210027144A1

    公开(公告)日：2021-01-28

    申请号：US16981080

    申请日：2018-05-15

    Applicant: LANDMARK GRAPHICS CORPORATION

    Inventor： Srinath Madasu ,  Yevgeniy Zagayevskiy ,  Terry Wong ,  Dominic Camilleri ,  Charles Hai Wang ,  Courtney Leeann Beck ,  Hanzi Mao ,  Hui Dong ,  Harsh Biren Vora

    IPC: G06N3/04 ,  G06N3/08 ,  G06F30/20 ,  G01V99/00

    Abstract: Using production data and a production flow record based on the production data, a deep neural network (DNN) is trained to model a proxy flow simulation of a reservoir. The proxy flow simulation of the reservoir is performed, using an ensemble Kalman filter (EnKF), based on the trained DNN. The EnKF assimilates new data through updating a current ensemble to obtain history matching by minimizing a difference between a predicted production output from the proxy flow simulation and measured production data from a field. Using the updated current ensemble, a second proxy flow simulation of the reservoir is performed based on the trained DNN. The assimilating and the performing are repeated while new data is available for assimilating. Predicted behavior of the reservoir is determined based on the proxy flow simulation of the reservoir. An indication of the predicted behavior is provided to facilitate production of fluids from the reservoir.

    Estimating Reservoir Production Rates Using Machine Learning Models for Wellbore Operation Control
    6.
    发明申请
    Estimating Reservoir Production Rates Using Machine Learning Models for Wellbore Operation Control 有权

    公开(公告)号：US20220205354A1

    公开(公告)日：2022-06-30

    申请号：US17136895

    申请日：2020-12-29

    Applicant: Landmark Graphics Corporation

    Inventor： Soumi Chaki ,  Honggeun Jo ,  Terry Wong ,  Yevgeniy Zagayevskiy ,  Dominic Camilleri

    IPC: E21B47/022 ,  E21B47/12 ,  G06N3/08 ,  G01V1/46 ,  G01V1/48

    Abstract: A system is described for estimating well production and injection rates of a subterranean reservoir using machine learning models. The system may include a processor and a non-transitory computer-readable medium comprising instructions that are executable by the processor to cause the processor to perform various operations. The processor may receive a set of static geological data about at least one subterranean reservoir in a subterranean formation. The processor may apply a trained convolutional neural network to the set of static geological data and data on initial states of dynamic reservoir properties to determine dynamic outputs of the subterranean reservoir. The processor may determine well data by extracting the set of static geological data and the dynamic outputs at well trajectories. And, the processor may apply a trained artificial neural network to the well data and subterranean grid information about the subterranean reservoir to generate estimated well production and injection rates.

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