中科微点-全球专利检索

  1. Login
  2. Sign Up

Search Results

13 records (took 0.017 seconds)

    WELL PLANNING BASED ON HAZARD PREDICTIVE MODELS
    1.
    发明公开
    WELL PLANNING BASED ON HAZARD PREDICTIVE MODELS 审中-公开

    公开(公告)号：US20230195952A1

    公开(公告)日：2023-06-22

    申请号：US17999291

    申请日：2021-05-20

    Applicant: Schlumberger Technology Corporation

    Inventor： Valerian Guillot ,  Florian Karpfinger ,  Ali Ozbek ,  Riadh Boualleg ,  Ling Li

    IPC: G06F30/13 ,  E21B44/00 ,  E21B7/04 ,  E21B41/00 ,  E21B49/00

    CPC classification number: G06F30/13 ,  E21B7/04 ,  E21B41/00 ,  E21B44/00 ,  E21B49/00 ,  E21B2200/20

    Abstract: A method to design well trajectories includes determining dogleg severity as a function of inclination, and a corresponding rate of penetration performance of the tool by a hybrid model including physical modelling and machine learning correction. The method includes solving for optimal steering parameters to predict a dogleg severity as close as possible to a desired dogleg severity at a given inclination of the trajectory, which is repeated for dogleg severity and inclination combinations of interest. The rate of penetration for feasible points is also determined and a rate of penetration (or time-to-target) map can be produced. Potential trajectories are then evaluated relative to the map to estimate drilling time-to-target, and an optimal trajectory can be selected that has a lowest time-to-target while also being feasible for the tool and optionally avoiding risks or downhole obstacles.

    Optimization based on predicted tool performance
    4.
    发明授权
    Optimization based on predicted tool performance 有权

    公开(公告)号：US12106028B2

    公开(公告)日：2024-10-01

    申请号：US18060977

    申请日：2022-12-02

    Applicant: Schlumberger Technology Corporation

    Inventor： Ali Ozbek ,  Riadh Boualleg ,  Ling Li

    IPC: E21B7/04 ,  E21B41/00 ,  E21B44/00 ,  E21B49/00 ,  G06F30/13 ,  G06F30/27

    CPC classification number: G06F30/27 ,  E21B7/04 ,  E21B41/00 ,  E21B44/00 ,  E21B49/00 ,  G06F30/13 ,  E21B2200/20 ,  E21B2200/22

    Abstract: A method to design well trajectories includes determining dogleg severity as a function of inclination, and a corresponding rate of penetration performance of the tool by a hybrid model including physical modelling and machine learning correction. The method includes solving for optimal steering parameters to predict a dogleg severity as close as possible to a desired dogleg severity at a given inclination of the trajectory, which is repeated for dogleg severity and inclination combinations of interest. The rate of penetration for feasible points is also determined and a rate of penetration (or time-to-target) map can be produced. Potential trajectories are then evaluated relative to the map to estimate drilling time-to-target, and an optimal trajectory can be selected that has a lowest time-to-target while also being feasible for the tool and optionally avoiding risks or downhole obstacles.

    DRILL BIT OPTIMIZER
    6.
    发明公开
    DRILL BIT OPTIMIZER 审中-公开

    公开(公告)号：US20240202407A1

    公开(公告)日：2024-06-20

    申请号：US18531797

    申请日：2023-12-07

    Applicant: Schlumberger Technology Corporation

    Inventor： Riadh Boualleg ,  Andrew Poor ,  Ling Li ,  Chris Bogath ,  Telman Maghrebi ,  James Masdea

    IPC: G06F30/27

    CPC classification number: G06F30/27 ,  E21B49/00 ,  E21B2200/20

    Abstract: A method for optimizing a drill bit for a drilling operation includes training a plurality of machine learning (ML) models with historical drilling data to obtain a corresponding plurality of trained ML models; obtaining drilling operation parameters for the drilling operation; generating drill bit parameters for each of a plurality of potential drill bit configurations; inputting the obtained drilling operation parameters and the generated drill bit parameters into the plurality of trained ML models to estimate a corresponding plurality of drill bit performance metrics; and selecting an optimum drill bit configuration from the set of potential drill bit configurations based on the estimated drill bit performance metrics obtained from plurality of trained ML models.

    OPTIMIZATION BASED ON PREDICTED TOOL PERFORMANCE
    7.
    发明公开
    OPTIMIZATION BASED ON PREDICTED TOOL PERFORMANCE 审中-公开

    公开(公告)号：US20230186000A1

    公开(公告)日：2023-06-15

    申请号：US18060977

    申请日：2022-12-02

    Applicant: Schlumberger Technology Corporation

    Inventor： Ali Ozbek ,  Riadh Boualleg ,  Ling Li

    IPC: G06F30/27 ,  E21B7/04 ,  E21B41/00 ,  G06F30/10

    CPC classification number: G06F30/27 ,  E21B7/04 ,  E21B41/00 ,  G06F30/10 ,  E21B2200/20 ,  E21B2200/22

    Abstract: A method to design well trajectories includes determining dogleg severity as a function of inclination, and a corresponding rate of penetration performance of the tool by a hybrid model including physical modelling and machine learning correction. The method includes solving for optimal steering parameters to predict a dogleg severity as close as possible to a desired dogleg severity at a given inclination of the trajectory, which is repeated for dogleg severity and inclination combinations of interest. The rate of penetration for feasible points is also determined and a rate of penetration (or time-to-target) map can be produced. Potential trajectories are then evaluated relative to the map to estimate drilling time-to-target, and an optimal trajectory can be selected that has a lowest time-to-target while also being feasible for the tool and optionally avoiding risks or downhole obstacles.

Patent Agency Ranking