Wavelet-fused richer convolution feature for multiphase flow visual monitoring of oil and gas production processes

Abstract

Multiphase flow monitoring of oil and gas production process is of great significance to the safety of oil and gas exploitation and production. ECT is one of the most attractive technology in the field of multiphase flow measurement due to the advantages of non-radioactive, non-contact, visualization, and low cost. We propose a reconstruction algorithm based on wavelet and RCF (W-RCF) for solving the problem of artifacts and edge blurring in ECT imaging. In the algorithm, the two-channel source images of Tikhonov regularization and Landweber are simultaneously decomposed by three-level wavelet. On this basis, an image fusion rule combined Bayesian decision and maximum entropy threshold is established to optimize the wavelet coefficients at each scale, which the rule can reduce image artifacts and compensate for the defects of the source images. Afterwards, the fused images are input to the RCF network for training and testing, and the ECT reconstructed images with higher quality are obtained. According to simulation and experiment results, it can be seen that the image reconstruction quality of W-RCF is significantly better than that of the LBP, Tikhonov regularization, Landweber and CNN algorithm. Therefore, W-RCF algorithm has higher accuracy and stronger adaptability for multiphase flow under different flow patterns, which provides an effective way for ECT image reconstruction and is more suitable for visual monitoring of multiphase flow in oil and gas production process.