A Pressure Wave Recognition and Prediction Method for Intelligent Sliding Sleeve Downlink Communication Systems Based on LSTM

To address the challenges of signal recognition and prediction in intelligent sliding sleeve downlink communication systems, this paper proposes a dual-model framework based on Long Short-Term Memory (LSTM) networks. The system comprises a classifier for identifying pressure wave edge types and a generator for predicting pressure waveforms. High-quality training data are generated by simulating pressure wave propagation caused by throttle valve modulations. A sliding window strategy and Z-score normalization are applied to enhance temporal modeling. The classifier achieves a high accuracy in identifying rising and falling edges under noise-free conditions. The generator, trained on down-sampled waveform segments, accurately reconstructs pressure dynamics using a dual-input strategy based on historical segments and hypothetical labels. A residual-based decision mechanism is employed to complete the full sequence label prediction. To evaluate robustness, noise intensities of 30 dB and 40 dB are introduced. The proposed system maintains high performance under both conditions, achieving label prediction accuracies of 100%. Error metrics such as MAE and RMSE remain within acceptable bounds, even in noisy environments. The results demonstrate that the proposed LSTM-based method has been validated on simulated data, showing its potential to approximate performance in real-world conditions. It provides a promising solution for cable-free measurement-while-drilling (MWD) and remote control of intelligent sliding sleeves in complex downhole environments.