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• W2567066084 abstract "Oil and gas well production prediction takes place in early stages of production to estimate future recovery. A data driven workflow is proposed in this paper to construct a symbolic tree model to predict new well production using historic time-series production data of analogous wells. Production data are firstly aggregated and symbolized for dimensionality reduction and data discretization of time-series data. A symbolic tree is constructed on time-series symbol sequences, and pre-pruning mechanisms – minimum node size and spatial information gain – are integrated to achieve a compact and informative tree. A coverage index is used to assess the tree size. A case study was conducted applying the proposed workflow to shale gas wells in Montney-A pool in Canada. It has proved the feasibility and accuracy of the proposed method." @default.
• W2567066084 created "2017-01-06" @default.
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• W2567066084 date "2016-10-01" @default.
• W2567066084 modified "2023-09-26" @default.
• W2567066084 title "A Symbolic Tree Model for Oil and Gas Production Prediction Using Time-Series Production Data" @default.
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• W2567066084 doi "https://doi.org/10.1109/dsaa.2016.36" @default.
• W2567066084 hasPublicationYear "2016" @default.
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