The rotary water jetting is one of the most important techniques for horizontal well cleanup. Currently, the operation optimization of this technique depends mainly on experience due to absence of applicable evaluation and design models for removing plugging materials. This paper presents an experimental setup to simulate the cleanup process of plugged screens by rotary water jetting on the surface and to evaluate the performance of a jetting tool. Using real plugged screens pulled from damaged etc., were obtained. The test results indicated that the cleanup performance was much better when the rotary jetting tool moved and stopped periodically for a certain time than that when it reciprocated at a constant speed. To be exact, it was desirable for the rotary jetting tool to move for 1.5-2 m and stop for 2-4 min, which was called the "move-stop-move" mode. Good cleanup performance could be obtained at also indicated that complex mud acid was better than clean water in terms of cleanup performance. Good Rotary jetting is preferred for the cleanup of horizontal wells with severely plugged screens, and the screen permeability recovery ratio may reach 20% if optimized operation parameters were used.
Distribution network reconfiguration technology is the key technology of distribution network fault treatment and power supply recovery. Given the problem that the traditional distribution network reconfiguration algorithm needs a lot of power flow calculation and low efficiency to find the optimal target, the simulated annealing algorithm is proposed to optimize the computing speed and searching ability of the distribution network reconfiguration algorithm. Firstly, the information interaction technology of distribution network protection and control equipment is studied, and the real-time data rapid peer-to-peer exchange and data and information interaction model is built, which improves the fault detection and timely removal ability of the distribution network. Secondly, the simulated annealing algorithm is used to reduce the number of power flow calculations, reduce the calculation time, improve the calculation speed of network reconstruction problems, and ensure that the optimal or better network topology can be obtained. Finally, the simulation analysis of the IEEE37 node system verifies the effectiveness of the proposed reconfiguration protection method in a complex environment with massive access to distributed energy.
One of the top issues in logistics management and related research is to establish an effective distribution system that is adaptive to new retail and capable of lowering the cost of logistics while enhancing consumer satisfaction. Aimed at reversing the weak points of current logistics distribution patterns, a dual-objective bipolar model with optimal logistics cost and consumer satisfaction by restraining distribution time and load is tested in this paper to figure out the proper nodes and vehicle routes. Data from general and front warehouses of PuPu mall, a Fuzhou-based online retail enterprise, are made into a case study. Moreover, the immune algorithm and genetic algorithm are adopted to achieve the model solution. It is found that the immune algorithm is more efficient than the genetic algorithm in searching solutions, thus having better adaptivity and effectiveness, and also that the type of distribution vehicle plays a significant role in determining the total distribution cost.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.