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.

    CLASSIFICATION OF CHARACTER STRINGS USING MACHINE-LEARNING

    公开(公告)号:US20190080164A1

    公开(公告)日:2019-03-14

    申请号:US16131946

    申请日:2018-09-14

    Abstract: Systems and methods for categorizing patterns of characters in a document by utilizing machine based learning techniques include generating character classification training data, building a character classification model based on the character classification training data; obtaining an image that includes a pattern of characters, the characters including one or more contours, applying the character classification model to the image to classify the contours, and applying the labels to clusters of the contours.

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