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UNCERTAINTY-AWARE MODELING AND DECISION MAKING FOR GEOMECHANICS WORKFLOW USING MACHINE LEARNING APPROACHES
UNCERTAINTY-AWARE MODELING AND DECISION MAKING FOR GEOMECHANICS WORKFLOW USING MACHINE LEARNING APPROACHES
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机译：使用机器学习方法的地质力学工作流的不确定性感知建模与决策
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摘要
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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