Real-time warning method for sand plugging in offshore fracturing wells

Sand plugging during hydraulic fracturing is one of the primary causes of operational failure. Existing methods for identifying sand plugging during fracturing suffer from issues such as time-consuming, low accuracy, and inability to provide real-time warning. Addressing these challenges, this study leverages offshore hydraulic fracturing operational data and reports to propose a novel method for intelligent identification and real-time warning of sand plugging. Initially, we employ an Attention Mechanism based Long-Short Term Memory Network (Att-LSTM) to establish a real-time pressure prediction model during fracturing, capable of forecasting pressure within 40 s with an accuracy exceeding 92%. Subsequently, we enhance the structure of an Attention Mechanism based Convolutional Long-Short Term Memory Network (Att-CNN-LSTM) to develop a model for identifying sand plugging during fracturing, achieving identification with an error margin of less than 1 min. Finally, through the integration of Att-LSTM and Att-CNN-LSTM networks coupled with transfer learning techniques, we introduce a continuously learning approach for sand plugging warning during fracturing operations, significantly improving accuracy and efficiency in sand plugging identification and advancing the intelligent decision-making process for hydraulic fracturing. These methodologies not only contribute theoretical innovations but also demonstrate substantial practical effectiveness, providing critical technical support and guidance to enhance safety and efficiency in hydraulic fracturing operations.