Oil and Water Flow Characterization Using Distributed Acoustic Sensing

Accurate flow rate estimation and water cut measurement are essential for optimizing production processes and ensuring safe pipeline operation. This study investigates the application of Distributed Acoustic Sensing (DAS) technology for real-time monitoring and characterization of oil-water two-phase flow in horizontal pipes. Experiments were conducted in a multiphase flow loop featuring a 14-meter-long, 2-inch-diameter horizontal test section. A helically wrapped fiber-optic cable was installed in the test section to monitor the fluid flow behavior, followed by a high-speed camera recording section for cross-validation of DAS signal observations. Mineral oil and water were used as the test fluids. The experimental results have demonstrated that the DAS signal effectively captured the transient dynamic behaviors of the oil and water flow, particularly in flow patterns that demonstrated large disturbances at the oil and water interface. Additionally, the DAS responses have shown a strong correlation with liquid mixture velocity and water cut. In this study, DAS signals were analyzed using spectral analysis techniques to identify frequency bands that correlate with oil and water phases. Polynomial regression models were developed to estimate flow rates and water cut based on DAS data, showing high accuracy. DAS-based multiphase flow monitoring has gained significant attention in recent years due to its non-intrusive nature, real-time capabilities, and suitability for harsh environments. However, most previous studies have focused on gas-liquid flow, while oil-water flow characterization and measurement remain challenging due to the similar properties of oil and water. For the first time, this study presents a comprehensive analysis of DAS signals for oil-water flow, providing new insights that will advance DAS-based multiphase flow monitoring in the future.