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    ENHANCED CENTRIFUGE FOR CORE SAMPLE ANALYSIS
    1.
    发明申请
    ENHANCED CENTRIFUGE FOR CORE SAMPLE ANALYSIS 有权

    公开(公告)号：US20220168754A1

    公开(公告)日：2022-06-02

    申请号：US17601875

    申请日：2019-05-10

    Applicant: LANDMARK GRAPHICS CORPORATION

    Inventor： Travis St. George RAMSAY ,  Chuck BALDWIN

    IPC: B04B5/02 ,  G01N15/08

    Abstract: Disclosed herein are example configurations of centrifuges used for analyzing properties of core samples extracted from sub-surface environments, in which the configuration of the centrifuges and rotations thereof improve fluid distribution within core samples held in the apparatus. In one aspect, a centrifuge includes a rotating arm and a holder coupled to a distal end of the rotating arm, the holder being configured to rotate independently of the rotating arm for analyzing fluid-rock interaction within the holder.

    A SIMULATION-TO-SEISMIC WORKFLOW CONSTRUED FROM CORE BASED ROCK TYPING AND ENHANCED BY ROCK REPLACEMENT MODELING
    4.
    发明申请
    A SIMULATION-TO-SEISMIC WORKFLOW CONSTRUED FROM CORE BASED ROCK TYPING AND ENHANCED BY ROCK REPLACEMENT MODELING 审中-公开
    基于岩石的岩石类型和岩石替代建模提高的模拟地震工作流

    公开(公告)号：US20160161635A1

    公开(公告)日：2016-06-09

    申请号：US14893929

    申请日：2013-08-13

    Applicant: LANDMARK GRAPHICS CORPORATION

    Inventor： Travis St. George RAMSAY ,  Jeffrey Marc YARUS

    IPC: G01V99/00 ,  G06F17/10

    CPC classification number: G01V99/005 ,  G01V11/00 ,  G01V2210/663 ,  G06F17/10

    Abstract: The disclosed embodiments include a system and method for performing a simulation to seismic process. In one embodiment, the system is configured to perform operations comprising constructing a petrophysical realization and selecting a candidate model for fluid flow simulation using the petrophysical realization. Empirical petrofacies definitions is applied on the selected candidate model and assigning relative permeability at each node of the petrofacies definitions of the selected candidate model. The operations performs flow simulation on selected candidate model and performs analysis on the results of the simulation on selected candidate model to verify rock type flow units. The dynamic and static simulation results are synthesized such that the combined data yield a measurable rock property that may be compared to seismic properties and used to calibrate subsequent iterations of the static earth model. The continued iteration of the workflow may then be undertaken with the updated/refined earth model.

    Abstract translation: 所公开的实施例包括用于对地震过程进行模拟的系统和方法。 在一个实施例中，系统被配置为执行包括构建岩石物理实现并且使用岩石物理实现来选择用于流体流动模拟的候选模型的操作。 对所选择的候选模型应用经验Petrofacies定义，并在所选候选模型的石油界定义的每个节点处分配相对渗透率。 操作对所选候选模型进行流量模拟，并对所选候选模型的模拟结果进行分析，以验证岩石流量单位。 合成动态和静态模拟结果，使得组合数据产生可测量的岩石性质，可以与地震属性进行比较，并用于校准静态地球模型的后续迭代。 然后可以使用更新/改进的地球模型进行工作流的持续迭代。

    METHOD AND SYSTEM FOR PREDICTION AND CLASSIFICATION OF INTEGRATED VIRTUAL AND PHYSICAL SENSOR DATA
    7.
    发明公开
    METHOD AND SYSTEM FOR PREDICTION AND CLASSIFICATION OF INTEGRATED VIRTUAL AND PHYSICAL SENSOR DATA 审中-公开

    公开(公告)号：US20240093605A1

    公开(公告)日：2024-03-21

    申请号：US17766775

    申请日：2019-11-07

    Applicant: LANDMARK GRAPHICS CORPORATION

    Inventor： Travis St. George RAMSAY ,  Egidio MAROTTA ,  Srinath MADASU

    IPC: E21B49/08 ,  E21B43/16

    CPC classification number: E21B49/0875 ,  E21B43/162 ,  E21B2200/22

    Abstract: The present disclosure is related to improvements in methods for evaluating and predicting responses of virtual sensors to determine formation and fluid properties as well as classifying the predicted as plausible or outlier responses that can indicate the need for maintenance of downhole physical sensors. In one aspect, a method includes detecting a change to a system of operating a wellbore to yield a determination, the system including a virtual sensor, the virtual sensor including a physical sensor placed in the wellbore for collecting one or more physical properties inside the wellbore; and based on the determination, performing one of retraining a machine learning model for predicting an output of the virtual sensor or predicting an output of the virtual sensor using the machine learning mode, the predicted output being indicative of at least one of sub-surface formation or fluid properties inside the wellbore.

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