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RECURRENT NEURONAL NETWORK MODEL FOR DOWNHOLE PRESSURE AND TEMPERATURE IN LOWERING ANALYSIS

机译:降落分析中井下压力和温度的递归神经网络模型

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
机译:本发明涉及一种压裂地层的方法。在压裂操作期间从井眼获取实时压裂数据。实时压裂数据是使用循环神经网络处理的,该网络使用来自相似井的历史数据进行训练。使用处理后的实时压裂数据确定实时响应变量预测。基于实时响应变量预测,实时调整压裂作业的压裂参数。使用基于实时响应变量预测已调整的压裂参数执行压裂操作。该图将以摘要形式发布:图1B

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