Downhole Pressure Pulse Signal Recognition Based on SSA-CNN-LSTM - JIANG Panqin; LIU Xingbin; JIANG Zhicheng; LI Shanwen; HE Zhuang (Cejing jishu, 2025)
Aiming at the problems of signal distortion and increased bit error rate caused by the interference of downhole sensor noise, fluid turbulent fluctuations, and wall friction effects on the wireless pressure pulse signals in the intelligent stratified water injection system, this paper designs a hybrid prediction model that integrates the sparrow search algorithm (SSA) and the convolutional long short-term memory network (CNN-LSTM). This model extracts the local spatial features of the pressure pulses such as steep rising edges and oscillating waveforms through the convolutional neural network (CNN) module, analyzes the long-term dependencies of periodic pump-valve operations in combination with the long short-term memory (LSTM) layer, and uses the SSA algorithm to adaptively optimize three hyperparameters, namely the learning rate, regularization coefficient, and the number of hidden layer nodes, so as to enhance the signal feature decoupling ability in the presence of noise. Comparative experiments are conducted using the experimental dataset of the intelligent stratified water injection project. It is found that the SSA-CNN-LSTM algorithm model outperforms traditional LSTM, CNN-LSTM, and PSO (particle swarm optimization) -CNN-LSTM models in terms of both fitting ability and prediction accuracy. Its coefficient of determination can reach as high as 99%, and the mean absolute error (MAE) and mean squared error (MSE) are as low as 0.011 483 MPa and 0.000 291 MPa2 respectively. The experimental results show that through the mechanisms of spatio-temporal feature fusion and parameter adaptive optimization, this model effectively suppresses the accuracy degradation of pressure pulse prediction caused by downhole unsteady-state interference, provides a highly robust signal processing solution for the wireless transmission scenarios of intelligent water injection systems, verifies the technical feasibility of analyzing industrial time-series data under complex working conditions, and provides theoretical support for the real-time monitoring and precise control of downhole intelligent equipment.