Foam flooding by Foam Assisted Water-Alternating-Gas (FAWAG) is an important enhanced oil recovery method that has proven successful in experimental and pilot studies. The present study is carried out to monitor the movement of the foam front once injected into the porous medium. This study aims to investigate applications of resistivity waves to monitor foam propagation in a sandstone formation. In the present lab-scale experiments and simulations, resistivity measurements were carried out to monitor the progression of foam in a sand pack, and the relationships between foam injection time and resistivity, as well as brine saturation, were studied. The brine saturation from foam simulation using CMG STAR is exported to COMSOL and calculated true formation resistivity. A diagram was produced summarizing the progression of foam through the sand pack in the function of time, which enabled us to establish how foam progressed through a porous medium. A surfactant and brine mixture was injected into the sand pack, followed by nitrogen gas to generate the foam in situ. As foam progressed through the sand pack, resistance measurements were taken in three zones of the sand pack. The resistance was then converted into resistivity and finally into brine saturation. As foam travels through the sand pack, it is predicted to displace the brine initially in place. This gradually increases each zone's resistivity (decreases the brine saturation) by displacing the brine. Also, an increase in the surfactant concentration results in higher resistivity. Finally, a comparison of three different surfactant concentrations was evaluated in terms of resistivity results, water saturation, and foam propagation monitoring to obtain the optimum surfactant concentration involved in foam flooding.
Maritime objects frequently exhibit low-quality and insufficient feature information, particularly in complex maritime environments characterized by challenges such as small objects, waves, and reflections. This situation poses significant challenges to the development of reliable object detection including the strategies of loss function and the feature understanding capabilities in common YOLOv8 (You Only Look Once) detectors. Furthermore, the widespread adoption and unmanned operation of intelligent ships have generated increasing demands on the computational efficiency and cost of object detection hardware, necessitating the development of more lightweight network architectures. This study proposes the EL-YOLO (Efficient Lightweight You Only Look Once) algorithm based on YOLOv8, designed specifically for intelligent ship object detection. EL-YOLO incorporates novel features, including adequate wise IoU (AWIoU) for improved bounding box regression, shortcut multi-fuse neck (SMFN) for a comprehensive analysis of features, and greedy-driven filter pruning (GDFP) to achieve a streamlined and lightweight network design. The findings of this study demonstrate notable advancements in both detection accuracy and lightweight characteristics across diverse maritime scenarios. EL-YOLO exhibits superior performance in intelligent ship object detection using RGB cameras, showcasing a significant improvement compared to standard YOLOv8 models.