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RECURRENT NEURONAL NETWORK MODEL FOR DOWNHOLE PRESSURE AND TEMPERATURE IN LOWERING ANALYSIS
RECURRENT NEURONAL NETWORK MODEL FOR DOWNHOLE PRESSURE AND TEMPERATURE IN LOWERING ANALYSIS
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机译:降落分析中井下压力和温度的递归神经网络模型
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摘要
The present invention relates to a method of fracturing a formation. Real-time fracturing data is acquired from a wellbore during the fracturing operation. Real-time fracturing data is processed using a recurrent neural net trained with historical data from similar wells. A real-time response variable prediction is determined using the real-time fracturing data processed. The fracturing parameters for the fracturing operation are adjusted in real time based on the real-time response variable prediction. The fracturing operation is performed using the fracturing parameters that have been adjusted based on the real-time response variable prediction. Figure to be published with the abstract: Fig. 1B
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