Study On Velocity Measurement Method Of Oil-water Two-phase Flow In Vertical Well Based On DPIV

The measurement of flow parameters of vertical oil-water two-phase flow under high water cut has always been an important part of oilfield logging engineering.The traditional flow parameter measuring instrument has many limitations such as stuck sand and single point measurement,which leads to inaccurate measurement results.Therefore,it is necessary to design a measuring instrument with high precision and strong stability.It is necessary to study the flow characteristics of the vertical oil-water two-phase flow in the design of the new measuring instrument.The flow velocity and flow velocity distribution of the oil-water twophase flow are important parameters to characterize the flow characteristics.Therefore,it is necessary to measure the flow velocity and flow velocity distribution of the oil-water two-phase flow in the simulated well.Currently most of the velocity measurement methods are single point measurement,it is not possible to observe the distribution of velocity in the flow field.Particle image velocimetry(PIV)is a kind of effective method to visualize the whole flow field velocity distribution in fluid mechanics field,applying PIV to the oil-water two phase flow velocity measurement in vertical wells can make the measurement process has the advantages of no contact,no disturbance,full measure,at the same time,because the research object turned from the common single-phase flow to oil-water two phase flow,its application also have limitations.It is necessary to study the application of PIV measurement in oil-water two-phase flow so as to overcome its limitation and play an important role in vertical oil-water two-phase flow measurement.The traditional PIV measurement technology needs to sprinkle tracer particles into the measured flow body,but in the oil-water two-phase flow fluid,the tracer particles are blocked,and after several tests in the vertical well,some analogous oil droplet attachments are attached to the well wall,which seriously affects the quality of the oil-water two-phase flow image.For the above problems,the oil phase in the oil-water two-phase flow is used to replace the tracer,as the research object is the gray level image of vertical oil-water two-phase flow,the image cross-correlation registration is prone to errors due to the complex gray level information,noise and rotation deformation in the oil drop of the image plane.The iterative closest point(ICP)is used to replace the traditional cross-correlation matching according to the image features,and the moving least squares is used to supplement the missing velocity value in the velocity field.For the problem of analogous oil droplets attached to the well wall,image clustering method is adopted to identify the outline of oil droplets attached to the well wall,extracting the color feature and motion feature of the oil-water two-phase flow image and Kmeans clustering algorithm is used to classify the outline of oil droplets and the outline of analogous oil droplets attached to the well wall.Due to the traditional K-means algorithm itself exists the problem of clustering result accuracy affected largely by outliers and initial clustering center selection,therefore,based on the idea of hierarchical clustering,the logarithmic data points are filtered before clustering,so as to improve the clustering results,the improved clustering method is called ILF-Kmeans algorithm,experiments show that compared with K-means algorithm the ILF-Kmeans algorithm improves the clustering precision by 5.72%.The oil-water two-phase flow experiment was carried out on the vertical simulated well with 125 mm pipe diameter,and the above research results were applied to the measurement of oil-water twophase flow velocity in the vertical well.The experimental results show that the measurement accuracy of oil-water two-phase flow under 125 mm pipe diameter is improved by 6.45%.