基于深度学习的海上压裂砂堵风险实时预警方法OA

To overcome the limitations of conventional sand-plug identification methods during hydraulic fracturing operation,such as low efficiency,high labor-intensity,limited accuracy,and inability to provide real-time early warning,we develop an automated sand-plugging risk identification and intelligent early-warning model for offshore fracturing wells based on multi-parameter operational data—including operational pressure,pumping rate and sand concentration—and deep learning algorithms.Firstly,an attention-based long short-term memory neural network(Att-LSTM)is employed to establish a real-time wellhead pressure prediction model,which could forecast pressure evolution 40 s in advance with an accuracy exceeding 92%.Secondly,an improved attention-based convolutional neural network-LSTM(Att-CNN-LSTM)model is proposed to identify sand-plug,achieving a temporal identification error of less than 1 min.By integrating these two models and incorporating a transfer learning module,a real-time sand-plugging risk early-warning system with continuous transfer learning capability is established.The results indicate that the proposed warning model,driven by the predicted pressure values,can identify sand-plugging events and outputs the sand-plugging probabilities for both the current moment and the subsequent 40 s,calculated as the average of the top five probability values.Field validation shows that the system can trigger warnings 38-42 s prior to actual sand-plugging events.In addition,the embedded transfer learning module helps reduce the number of training iterations required for formal model convergence from 2 000 to 300,improving computational efficiency by a factor of 5.7.This study demonstrates that the proposed deep learning approach can significantly enhance the accuracy and efficiency of sand-plug identification and early-warning,thereby accelerating the intelligent decision-making process in hydraulic fracturing operations.