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    • 1. 发明申请
    • HYDROCARBON FLUID PROPERTIES PREDICTION USING MACHINE-LEARNING-BASED MODELS
    • WO2022038476A1
    • 2022-02-24
    • PCT/IB2021/057475
    • 2021-08-13
    • ABU DHABI NATIONAL OIL COMPANYABU DHABI COMPANY FOR ONSHORE PETROLEUM OPERATION LIMITED
    • GHORAYEB, KassemMAWLOD, Arwa AhmedMUSTAPHA, HusseinMOHAN, Richard
    • G06N20/20G06F17/15G06F17/18G06F16/215G06K9/62E21B49/08G01N33/28G06F16/26
    • A computer-implemented method (100) for predicting hydrocarbon fluid properties using machine-learning-based models is provided. The computer-implemented method comprises the step of receiving (101) a incomplete set of pressure-volume-temperature (PVT) data for hydrocarbon fluid samples from a PVT data base; reading (102) the incomplete set of PVT data by a reader module; transforming (103) the incomplete set of PVT data into a unified data structure by the reader module; selecting (104) items of the PVT data from the incomplete set of PVT data by the reader module; processing (105) the selected items of the PVT data by a correlating module to identify a plurality of correlations in the selected items of the PVT data based on one or more of the fluid properties of the hydrocarbon fluid samples; clustering (106), using of at least one of a plurality of clustering schemes, the selected items of the PVT data into a plurality of clusters by a clustering module; and performing (107) machine learning by a machine learning module on ones of the plurality of clusters to predict missing fluid properties in the incomplete set of PVT data and thus to obtain a complete set of PVT data. Further a computer-implemented method (200) for generating equations of state (EoS) for a plurality of hydrocarbon fluids is provided. The computer-implemented method comprises the step of delumping (201) pressure-volume- temperature (PVT) data for hydrocarbon fluid samples from a complete set of PVT data to one of a set of detailed fluid components, or to a common set of components and pseudo- components; lumping (202) the PVT data from the complete set of PVT data into a pre- defined set of components and pre-defined set of pseudo-components to generate a plurality of equation of state (EoS) models; generating (203) for the PVT samples on the PVT data set an EoS model using a same set of tuning parameters and thereby generating an EoS fluid model fingerprint for the hydrocarbon fluid samples; and associating (204) properties of the hydrocarbon fluid samples with the generated EoS fluid model fingerprint.