Painting For Geomodeling
    3.
    发明申请

    公开(公告)号:US20230057978A1

    公开(公告)日:2023-02-23

    申请号:US17758563

    申请日:2020-01-22

    Abstract: The invention notably relates to a computer-implemented method of geomodelling. The method comprises providing a pseudo-stratigraphic grid. The pseudo-stratigraphic grid represents a reservoir and has pillars. Each pillar includes respective cells. Each cell has a respective stratigraphic layering index. The method then comprises providing a surface. The surface has a first region and a second region. The second region is complementary to the first region. The method also comprises, for each first pillar intercepted by the first region, determining a respective first stratigraphic layering value based on the relative position of the surface in the first pillar. The method also comprises, for each second pillar intercepted by the second region, determining a respective second stratigraphic layering value by interpolating and/or extrapolating first stratigraphic layering values. This provides an improved solution of geomodeling.

    Performing A Deformation-Based Physics Simulation

    公开(公告)号:US20220382933A1

    公开(公告)日:2022-12-01

    申请号:US17730897

    申请日:2022-04-27

    Inventor: Stefano Frambati

    Abstract: The disclosure relates to a computer-implemented method for performing a deformation-based physics simulation described by a partial differential equation. The method comprises providing a geometrical model representing a portion of the real world. The method comprises performing a hybrid discretization of the model. The performing of the hybrid discretization comprises discretizing one or more first objects in the portion each with a mesh and one or more second objects in the portion each with a point cloud. The method comprises one or more iterations. Each iteration comprises performing a simulation run based on a discretization of the partial differential equation and on the hybrid discretization. The iteration comprises assessing a deformation as a result of the simulation run. The deformation corresponds to a shape deformation of the one or more second objects. The iteration comprises updating the hybrid discretization to model the deformation by moving points of a point cloud.

    MOORING LINE FOR FLOATING PLATFORM

    公开(公告)号:US20240383576A1

    公开(公告)日:2024-11-21

    申请号:US18694531

    申请日:2022-09-23

    Abstract: The invention concerns a mooring line for a floating platform, preferably a floating wind turbine platform, the mooring line comprising: a first segment able to be attached to the platform; a second segment able to be attached to a sea ground; at least an intermediate segment formed of an elastomeric material and arranged between the first segment and the second segment. The intermediate segment is able to provide a maximal extension greater than 100% of the rest length of the intermediate segment, advantageously a maximal extension greater than 300%.
    The intermediate segment presents a minimal breaking strength greater than 18 MPa, advantageously greater than 25 MPa.

    METHOD FOR PREDICTING CLOGGING OF DISTILLATION COLUMN(S) IN A REFINERY, COMPUTER PROGRAM AND ASSOCIATED PREDICTION SYSTEM

    公开(公告)号:US20230119842A1

    公开(公告)日:2023-04-20

    申请号:US17910869

    申请日:2021-03-17

    Abstract: The invention relates to a method for predicting flooding in a distillation column by machine learning including a constructing and training phase of a machine learning model obtained from previously collected data and from a set of sensors, an operational phase for predicting flooding(s), by collecting a current data flow until a buffer is filled, pre-processing data from the data buffer by predetermined cleansing and classification, synchronizing the data of the current set of clean and classified data, determining a value of a current variable representative of at least one current performance of the at least one distillation column, forming a current set of transformed data by calculating predetermined derivatives, and predicting the current state of said distillation column by applying said learning model to said current set of transformed data.

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