A giant strike-slip fault-controlled Fuman Oilfield has been found in the Ordovician fractured carbonates of the Tarim Basin. However, conventional seismic methods are hardly able to distinguish the fractured reservoir and its connectivity in the ultra-depth (>7000 m) carbonate fault zones. We propose thin-likelihood and tensor-thickness process methods to describe the fracture network and large cave reservoir, respectively. Together with the two methods for 3D visualization of fracture-cave reservoirs, we had an application in the ultra-deep well deployment in Fuman Oilfield. The results show that the fracture network and cave reservoir can be 3D-imaged more clearly than conventional methods. The fracture network and cave reservoir show distinct segmentation by the fault assemblage in Fuman Oilfield. Furthermore, 3D modeling is favorable for the reservoir connectivity description along the carbonate fault zones. There are three distinct reservoir models: fault core-, fault damage zone- and overlap zone-controlling fractured reservoirs along the fault zones. This revealed variable fractured reservoirs that are related to fault maturity and segmentation. The method has been widely used in fracture-cave reservoir description and subsequent well optimization, suggesting a favorable method for economic oil exploitation in the ultra-depth reservoirs. This case study is not only useful for the complicated reservoir 3D description and modeling but also helpful for well employment to provide support for the target evaluation and optimization in ultra-depth fractured reservoirs.
As an influential technology of swarm evolutionary computing (SEC), the particle swarm optimization (PSO) algorithm has attracted extensive attention from all walks of life. However, how to rationally and effectively utilize the population resources to equilibrate the exploration and utilization is still a key dispute to be resolved. In this paper, we propose a novel PSO algorithm called Chaos Adaptive Particle Swarm Optimization (CAPSO), which adaptively adjust the inertia weight parameter w and acceleration coefficients c 1 , c 2 , and introduces a controlling factor γ based on chaos theory to adaptively adjust the range of chaotic search. This makes the algorithm have favorable adaptability, and then the particles can not only effectively prevent missing the global optimal solution, but also have a high probability of jumping out of the local optimal solution. To verify the stability, convergence speed, and accuracy of CAPSO, we conduct ample experiments on 6 test functions. In addition, to further verify the effectiveness and scalability of CAPSO, comparative experiments are carried out on the CEC2013 test suite. Finally, the results prove that CAPSO outperforms other peer algorithms to achieve satisfactory performance.
Mobile edge computing has been widely used in various IoT devices due to its excellent computing power and good interaction speed. Task offloading is the core of mobile edge computing. However, most of the existing task offloading strategies only focus on improving the unilateral performance of MEC, such as security, delay, and overhead. Therefore, focus on the security, delay and overhead of MEC, we propose a task offloading strategy based on differential privacy and reinforcement learning. This strategy optimizes the overhead required for the task offloading process while protecting user privacy. Specifically, before task offloading, differential privacy is used to interfere with the user's location information to avoid malicious edge servers from stealing user privacy. Then, on the basis of ensuring user privacy and security, combined with the resource environment of the MEC network, reinforcement learning is used to select appropriate edge servers for task offloading. Simulation results show that our scheme improves the performance of MEC in many aspects, especially in security and resource consumption. Compared with the typical privacy protection scheme, the security is improved by 7%, and the resource consumption is reduced by 9% compared with the typical task offloading strategy.
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.