深圳大学学报（理工版）2026，Vol.43Issue(1)：65-73,9.DOI:10.3724/SP.J.1249.2026.01065
基于深度学习的海上压裂砂堵风险实时预警方法
Real-time risk early-warning method for sand plugging during offshore hydraulic fracturing based on deep learning
摘要
Abstract
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.关键词
石油与天然气工程/深度学习/压裂砂堵自动识别/压力智能预测/砂堵风险实时预警/迁移学习/数据特征增强Key words
petroleum and natural gas engineering/deep learning/automatic sand plugging identification in hydraulic fracturing/intelligent pressure prediction/real-time sand-plug risk early warning/transfer learning/feature augmentation分类
能源科技引用本文复制引用
郭布民,徐延涛,王晓鹏,王新根,宫红亮,巴广东,赵明泽..基于深度学习的海上压裂砂堵风险实时预警方法[J].深圳大学学报（理工版）,2026,43(1):65-73,9.基金项目
Sub-Course of the National Key Research and Development Program(2023YFB4104203) （2023YFB4104203）
China National Offshore Oil Corporation(CNOOC)"14th Five-Year Plan"Major Science and Technology Project(G2415B-1120C032) 国家重点研发计划子课资助项目(2023YFB4104203) （CNOOC）
中国海洋石油集团有限公司"十四五"科技重大专项资助项目(G2415B-1120C032) （G2415B-1120C032）
