Classification of piping and instrumental diagram information using machine-learning

    公开(公告)号:US11195007B2

    公开(公告)日:2021-12-07

    申请号:US16376827

    申请日:2019-04-05

    Abstract: Systems and methods for identifying patterns of symbols in standardized system diagrams are disclosed. Disclosed implementations obtain or synthetically generate a symbol recognition training data set including multiple training images, generate a symbol recognition model based on the symbol recognition training data set, obtain an image comprising a pattern of symbols, group symbols into process loops based on the logical relationships captured by process loop identification algorithm, apply a character classification model to image contours to identify the characters and group characters into tags via hierarchical clustering, and store the identified tags, symbols and identified process loops in a relational database.

    WELLHEAD FATIGUE PREDICTION VIA INTERPOLATION USING CLUSTERED MACHINE LEARNING MODELS AND EXTRAPOLATION USING CLUSTER CENTERS

    公开(公告)号:US20240427968A1

    公开(公告)日:2024-12-26

    申请号:US18338972

    申请日:2023-06-21

    Abstract: Metocean conditions for a wellhead, such as current profile and wave characteristics, are used to determine wellhead fatigue damage rate for the wellhead. The wellhead fatigue damage rate is determined using an interpolation approach or an extrapolation approach. In the interpolation approach, the metocean conditions of the wellhead are input into one of multiple clustered machine learning models to determine the wellhead fatigue damage rate. In the extrapolation approach, a curve is generated to fit cluster centers of the multiple clustered machine learning models, and the wellhead fatigue damage rate is determined based on the curve and the distance between the metocean conditions of the wellhead and null metocean conditions.

    UNCERTAINTY-AWARE MODELING AND DECISION MAKING FOR GEOMECHANICS WORKFLOW USING MACHINE LEARNING APPROACHES

    公开(公告)号:US20210382198A1

    公开(公告)日:2021-12-09

    申请号:US16892050

    申请日:2020-06-03

    Abstract: A Gaussian process is used to provide a nonparametric approach for modeling nonlinear relationships among physical quantities involved in the geomechanics workflow supporting drilling & completion operations. Gaussian process provides a nonparametric framework that enables injection of a prior belief into the basic model format while allowing its specific format to be adaptive in a certain range following an estimated distribution. Both this model-related uncertainty and the pre-assumed input data distributions may be calibrated using non-parametric Bayesian framework with Gaussian process as prior. This approach not only the addresses the uncertainty stemming from the input physical properties but also tackles the uncertainties underlying the adopted physical model, all in this nonparametric Bayesian framework with Gaussian process encoded as prior.

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