Research on Latent Space Convolution Filtering Algorithm for Water Hammer Signal

Fracture diagnosis with water hammer pressure data is an important technology for petroleum exploration and development. Efficient filtering of acquired pressure data is a critical process to enhance diagnostic accuracy. The water hammer pressure monitored at the wellhead is observed data, whose generation process is influenced by multiple known or unknown factors such as fractures and the pipeline friction, and contains uncertain noise. Existing filtering algorithms mainly focus on the water hammer signal itself, ignore the uncertainty of its generation factors, and have poor filtering ability for the complex wellbore and formation environments. From the perspective of data generation, this study formulates the factors affecting water hammer generation as latent variables, and proposes a latent space convolutional filtering (LSCF) model. The model estimates the probability distribution of latent variables, samples to obtain latent variables, then uses a convolutional neural network to filter out noise factors, and finally infers the probability distribution of the clean water hammer signal. Filtering experiments were conducted on both water hammer simulated datasets and field datasets using the model, with correlation coefficient (CC), mean squared error (MSE) and signal-to-noise ratio (SNR) adopted as quantitative evaluation metrics, combined with qualitative analysis of spectrum and cepstrum. Compared with existing advanced filtering algorithms, the LSCF model achieves optimal performance across all filtering metrics, verifying the advancement of the filtering strategy, providing a new technical reference for noise reduction and filtering of fracturing pump shutdown water hammer data.