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METHOD FOR IDENTIFYING MISALLOCATED HISTORICAL PRODUCTION DATA USING MACHINE LEARNING TO IMPROVE A PREDICTIVE ABILITY OF A RESERVOIR SIMULATION
METHOD FOR IDENTIFYING MISALLOCATED HISTORICAL PRODUCTION DATA USING MACHINE LEARNING TO IMPROVE A PREDICTIVE ABILITY OF A RESERVOIR SIMULATION
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机译:使用机器学习识别错误的历史生产数据的方法来提高储层模拟的预测能力
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摘要
A method for training a predictive reservoir simulation in which high- confidence reservoir sample data is used to identify misallocated historical production data used in the simulation. A neural network algorithm is trained with high-confidence reservoir historical production data. High-confidence reservoir sample data is obtained by at least one sensor at a reservoir location over a time interval, after which the reservoir historical production data is parametrically varied over the time interval to determine a time-indexed discrepancy between the reservoir historical production data and the high- confidence reservoir sample data over the time interval. The time- indexed discrepancy and a defined threshold discrepancy are then used as inputs to a machine learning process to further train the neural network algorithm to identify reservoir historical production data whose discrepancy exceeds the threshold discrepancy and thereby constitutes misallocated historical production data. The misallocated data can be back allocated.
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