Graph-Guided Partial Representation-Based Oil–Gas–Water Flow State Monitoring via Dual-Modal Detection

The detection and monitoring of oil–gas–water three-phase flow is highly demanded in oil–gas well industry. Concentrating on combining the three substates of oil, gas, and water to form a holistic flow state, a monitoring framework based on graph-guided partial representation is proposed while maintaining linkage among flow states. In this framework, a differential pressure (DP)-pulse wave ultrasonic Doppler (PWUD) system is designed for obtaining information on the three-phase structure and the intrinsic relationships among different phases, so as to accurately characterize the substates of oil, gas, and water. Furthermore, within the unified paradigm of variational mode decomposition (VMD), standard VMD and reduced-order VMD (rVMD) are, respectively, utilized in the information mining of DP mode depicting 1-D flow fluctuations and PWUD mode representing 2-D spatiotemporal flow field. The time-varying nonstationarity of flow evolution is then quantified using statistical analysis. Based on converting flow data to flow features, graph guidance- and part-based nonnegative matrix factorizations (NMFs) are integrated to model and monitor the flow states in complicated oil–gas–water flow process. The proposed novel scheme about graph-guided partial representation-based horizontal oil–gas–water flow state monitoring via dual-modal detection is verified by dynamic experiments on a three-phase flow loop at Tianjin University.