Dual-Parameter Measurement of Oil–Water Two-Phase Flow Based on Multitask Learning

Oil–water two-phase flow is a common phenomenon in industrial processes. Precisely measuring its parameters is crucial for monitoring production conditions and optimizing production strategies. Due to the complexity of oil–water two-phase flow, the precise simultaneous measurements of water cut and total flow rate are challenging tasks. To address this issue, we conduct vertical upward oil–water two-phase flow experiments using a dual-helix microwave sensor measurement system to collect fluid signals and design a dual-parameter measurement model (DPMM) based on multi-task learning for the simultaneous prediction of water cut and total flow rate. Firstly, DPMM utilizes the shared feature extractor (SFE) to extract shared features, facilitating interaction across tasks. Then, DPMM employs the multi-head attention network (MHAN) and the expanding kernel network (EKN) to extract specific features for water cut and total flow rate, respectively. Furthermore, a joint loss function is used to achieve collaborative optimization. Experimental results show that DPMM demonstrates outstanding performance in two-parameter measurements and surpasses other competing models. We verified the efficiency of the proposed methods by parameter analysis and ablation experiments.