REAL-TIME DRILLING OPTIMIZATION IN A METAVERSE SPACE

    公开(公告)号:US20240070344A1

    公开(公告)日:2024-02-29

    申请号:US17821660

    申请日:2022-08-23

    CPC classification number: G06F30/20 G06F40/40 G06T15/00 G06F2111/18

    Abstract: A system can be used for optimizing a wellbore operation via a metaverse space that can include one or more avatars. The system can provide access to the metaverse space for an entity. The metaverse space can be a computer-generated representation of a location relating to a wellbore operation. The system can receive, via an avatar in the metaverse space, a query from the entity relating to the wellbore operation. The avatar can include software applications for performing tasks in the metaverse space. The system can execute, via the avatar, a request to a micro-service for at least one solution parameter based on the query. The request can cause the micro-service to generate the at least one solution parameter. The system can receive the at least one solution parameter from the micro-service. The system can output the at least one solution parameter for adjusting the wellbore operation.

    Dynamic time warping of signals plus user picks

    公开(公告)号:US11906683B2

    公开(公告)日:2024-02-20

    申请号:US16954874

    申请日:2019-12-12

    CPC classification number: G01V11/002 E21B47/12 E21B49/00 G01V1/48 G01V3/38

    Abstract: A method for correlating data comprises acquiring a first sequence signal and a second sequence signal, wherein the first sequence signal comprises at least a first data point including a first set of components and the second sequence signal comprises at least a second data point including a second set of components; acquiring a first set of user picks and a second set of user picks, wherein the first and the second sets of user picks each contain a respective first and second correspondence between a component in the first set of components and a component in the second set of components; combining the first and second sets of user picks with the first and second sequence signals to create a first hyper-complex signal and a second hyper-complex signal; and performing signal alignment on the first and second hyper-complex signals.

    Casing wear and pipe defect determination using digital images

    公开(公告)号:US11885214B2

    公开(公告)日:2024-01-30

    申请号:US17361441

    申请日:2021-06-29

    CPC classification number: E21B47/002 E21B44/00 E21B44/02 E21B47/04

    Abstract: The disclosure presents solutions for determining a casing wear parameter. Image collecting or capturing devices can be used to capture visual frames of a section of drilling pipe during a trip out operation. The visual frames can be oriented to how the drilling pipe was oriented within the borehole during a drilling operation. The visual frames can be analyzed for wear, e.g., surface changes, of the drilling pipe. The surface changes can be classified as to the type, depth, volume, length, shape, and other characteristics. The section of drilling pipe can be correlated to a depth range where the drilling pipe was located during drilling operations. The surface changes, with the depth range, can be correlated to an estimated casing wear to generate the casing wear parameter. An analysis of multiple sections of drilling pipe can be used to improve the locating of sections of casing where wear is likely.

    BUILDING SCALABLE GEOLOGICAL PROPERTY MODELS USING MACHINE LEARNING ALGORITHMS

    公开(公告)号:US20230367031A1

    公开(公告)日:2023-11-16

    申请号:US17585441

    申请日:2019-12-03

    CPC classification number: G01V99/005 G06N3/091

    Abstract: A method of predicting rock properties at a selectable scale is provided, including receiving coordinates of locations of respective sample points, receiving measurement data associated with measurements or measurement interpretations for each sample point, receiving for each sample point a scale that indicates the scale used to obtain the measurements and/or measurement interpretations, wherein different scales are received for different sample points. A deep neural network (DNN) is trained by applying the received coordinates, measurement data, and scale associated with each sample point and associating the sample point with a rock property as a function of the coordinates, measurement data, and scale applied for the sample point. The DNN is configured to generate rock property data for a received request point having coordinates and a selectable scale, wherein the rock property data is determined for the request point as a function of the coordinates and the selectable scale.

    Automated horizon layer extraction from seismic data for wellbore operation control

    公开(公告)号:US11733416B2

    公开(公告)日:2023-08-22

    申请号:US17131072

    申请日:2020-12-22

    CPC classification number: G01V1/345 G01V1/301 G01V2210/643

    Abstract: A method includes receiving a seismic data volume comprising seismic information of subterranean formations and receiving a set of seismic traces of the seismic data volume. The method also includes, determining, along each seismic trace of the set of seismic traces, a set of seed points comprising minimum or maximum onsets. Further, the method includes sorting the set of seed points into a sorted set of seed points by absolute amplitude values of the set of seed points. Furthermore, the method includes generating a horizon representation of every seismic event in the seismic data volume by automatically tracking horizons throughout an entirety of the seismic data volume from the sorted set of seed points in an order of the absolute amplitude values of the sorted set of seed points. Additionally, the method includes generating a graphical user interface that includes the horizon representation for display on a display device.

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