Influence of quadrat characteristics on the evolution of the dispersion effect for fiber–water dispersions

Dispersing fibers in a water dispersion is an important issue for many fiber-based materials that significantly affects the mechanical and many other properties of materials. However, the measurement and assessment of the dispersion effect remains a significant challenge. In this study, we presented an image analysis method based on quadrat analysis from ecology and geography, transforming the issue of the dispersion effect into the statistics of point distribution. Furthermore, we changed the type of sampling and adjusted the shape, size and numbers of each quadrat to investigate its influences on the evaluation results. Our results showed that the area of one quadrat had a more obvious effect on the evaluation results compared to the number of quadrats. In addition, having a quadrat of an optimum shape enlarged the difference in various dispersion effects; the results of a square quadrat exhibited stably in both complete coverage and random sampling. Quadrat analysis realizes good measurement of dispersion states as a result of image processing and offers an assessment of the dispersion effect in a fiber–water dispersion.

An Experimental Investigation of High and Low Salinity Waterflood Displacement Under the Steady-State Condition

Oil recovery by low salinity waterflood is significantly affected by fluid-fluid interaction through the micro-dispersion effect. This interaction influences rock wettability and relative permeability functions. Therefore, to gain a better insight into multiphase flow in porous media and perform numerical simulations, reliable relative permeability data is crucial. Unsteady-state or steady-state displacement methods are commonly used in the laboratory to measure water-oil relative permeability curves of a core sample. Experimentally, the unsteady-state core flood technique is more straightforward and less time-consuming compared to the steady-state method. However, the obtained data is limited to a small saturation range, and the associated uncertainty is not negligible. On the other hand, the steady-state method provides a more accurate dataset of two-phase relative permeability needed in the reservoir simulator for a reliable prediction of the high salinity and low salinity waterflood displacement performance. Considering the limitations of the unsteady state method, steady-state high salinity and low salinity brine experiments waterflood experiments were performed to compare the obtained relative permeability curves. The experiments were performed on a carbonate reservoir sample using a live reservoir crude oil under reservoir conditions. The test was designed so that the production and pressure drop curve covers a wider saturation range and provides enough data for analysis. Consequently, reliable relative permeability functions were obtained, initially, for a better comparison and prediction of the high salinity and the low salinity waterflood injections and then, to quantify the effect of low salinity waterflood under steady-state conditions. The results confirm the difference in relative permeability curves between high salinity and low salinity injections due to the micro-dispersion effect, which caused a decrease in water relative permeability and an increase in the oil relative permeability. These results also proved that low salinity brine can change the rock wettability from oil-wet or mixed-wet to more water-wet conditions. Furthermore, the obtained relative permeability curves extend across a substantial saturation range, making it valuable information required for numerical simulations. To the best of our knowledge, the reported data in this work is a pioneer in quantifying the impact of low salinity waterflood at steady-state conditions using a reservoir crude oil and reservoir rock, which is of utmost importance for the oil and gas industry.

Analysis of Ionospheric Disturbance on X-band SAR Image Registration

The atmosphere is a very important factor that affects the accuracy of X-band SAR image registration, and the ionosphere effect has the most intricate influence. In response to this problem, this paper introduces the mathematical model of ionospheric dispersion effect and scintillation effect. Then, echo simulation, imaging processing, and image registration are used to calculate the image offset caused by the ionosphere, which can determine whether the ionosphere effect needs to be compensated during image registration. Simulation experimental results show that in the X-band image registration, the dispersion effect needs to be compensated, and the impact of the scintillation effect can be ignored.