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.
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.
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.
The Longdong area in the Ordos basin is a typical fluvial reservoir with strong heterogeneity. In order to clarify the distribution law of underground reservoirs in the Longdong area, it is necessary to establish and optimize a 3D geological model to characterize the heterogeneity of reservoirs. This is of great significance for accelerating the exploitation of tight sandstone gas in the southwest of the Ordos basin. This study takes the P2h8 member of the Ct3 research area in the Longdong area as an example, analyzes the core and logging curve shape to divide the sedimentary microfacies, and establishes the facies model. In particular, in view of the difficulty in obtaining 3D training images under the existing conditions in the study area, we use the multi-point geostatistics method combining sequential two-dimensional condition simulation and the direct sampling method to establish the facies model. This method can simulate the 3D geological model by using the 2D training images composed of the digital plane facies diagrams and the well-connection facies diagrams. In addition, we choose the object-based method and sequential indicator method for comparative experiments to verify the feasibility of this method (sequential two-dimensional condition simulation combined with the direct sampling method) from many aspects. The results show that the multi-point geostatistics method based on 2D training images can not only match the well data, but also show the geometric characteristics and contact relationship of the simulation object. The distribution characteristics of sandbody thickness and modeling results are consistent with the actual geological conditions in the study area. This study explores the feasibility of this method in the 3D geological simulation of large-scale fluvial facies tight sandstone reservoirs. Additionally, it also provides a new idea and scheme for the modeling method of geologists in similar geological environments.
This paper provide improved phase behavior models, trying to mitigate the problem that phase behavior of gas-crude system is difficult to describe in L block with low permeability and high water cut in China. This situation leads to a series of problems in CO2 flooding process and lower recovery up to expectations. The models is evaluated to possess both high calculation speed and accuracy compared with existing others. Characteristics of CO2-crude systems had been considered into repulsion-attraction type EOS (equation of state) based on the analysis of repulsion parameter and attraction parameter in EOS, and the improved EOS had been applied in developing calculation method of MMP (minimum miscible pressure). No ideality of CO2-crude systems had been considered into mixing rules of CO2-crude systems based on analysis of mixing rules of repulsion-attraction type EOS. Promotion had also been put into the obtain methods of parameters in phase behavior, including density, viscosity, MMP, critical parameters of plus components etc. All these methods are applied in L Block. The phase behavior models of CO2-crudes system promoted in this paper mainly include EOS, mixing rule and viscosity model and have been applied in CO2 flooding process in T Reservoir. The relative error of density calculation is reduced from 7% ∼ 20% to less than 1%and the modified EOS is applied to predict the MMP of the CO2-crude systems from 8 different blocks in T reservoir. The modified EOS also works well for the relative error of MMP prediction is reduced from 20% ∼ 70% to less than 5%. Compared with the existing mixing rules, the modified mixing rule is with higher calculation speed and accuracy. The relative error of components mole fraction calculation is reduced from 30% ∼ 80% to less than 10%. Compared with the existing viscosity models, there are large improvements of the modified viscosity model in accuracy. The relative error of viscosity simulation is reduced from more than 50% to about 5%. According to the simulation results, C2∼C15 are the key hydrocarbons with positive effect on the miscibility of CO2-crude systems, while C16+ are the key hydrocarbons with negative effect. The recovery of the pilot has increased by 23% by these methods. The improved phase behavior models provided in this paper possess as good performance as existing models in calculation speed, and accustom a big step forward in simulation accuracy. The modified components of the models also partially complete physical meaning in describing phase behavior of CO2-crude system. All the models mentioned above are finally applied in L block with HP/HT and high water cut and obtained an increase in recovery by 19.2%.
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