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Research on an optimization Method for Injection-Production Parameters Based on an Improved particle swarm optimization algorithm

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ENERGIES 2022年第8期15卷

作者： Dong, Yukun Zhang, Yu Liu, Fubin Zhu, Zhengjun China Univ Petr Coll Comp Sci & Technol Qingdao 266580 Peoples R China PetroChina Tarim Oilfield Co Res Inst Explorat & Dev Korla 841000 Peoples R China

The optimization of injection-production parameters is an important step in the design of gas injection development schemes, but there are many influencing factors and they are difficult to determine. To solve this problem, this paper optimizes injection-production parameters by combining an improved particle swarm optimization algorithm to study the relationship between injection-production parameters and the net present value. In the process of injection-production parameter optimization, the particle swarm optimization algorithm has shortcomings, such as being prone to fall into local extreme points and slow in convergence speed. Curve adaptive and simulated annealing particle swarm optimization algorithms are proposed to further improve the optimization ability of the particle swarm optimization algorithm. Taking the Tarim oil field as an example, in different stages, the production time, injection volume and flowing bottom hole pressure were used as input variables, and the optimal net present value was taken as the goal. The injection-production parameters were optimized by improving the particle swarm optimization algorithm. Compared with the particle swarm algorithm, the net present value of the improved scheme was increased by about 3.3%.

关键词： particle swarm optimization algorithm injection-production optimization curve adaptation

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Accelerating reservoir production optimization by combining reservoir engineering method with particle swarm optimization algorithm

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JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2022年 208卷

作者： An, Zhibin Zhou, Kang Hou, Jian Wu, Dejun Pan, Yuping China Univ Petr East China Key Lab Unconvent Oil & Gas Dev Minist Educ Qingdao 266580 Peoples R China Shandong Univ Sci & Technol Coll Energy & Min Engn Qingdao 266590 Peoples R China Qingdao Natl Lab Marine Sci & Technol Lab Marine Mineral Resources Qingdao 266237 Peoples R China China Univ Petr East China Sch Petr Engn Qingdao 266580 Peoples R China

Generally, the optimization of injection and production parameters in oilfields are carried out by using reservoir engineering method or mathematical algorithm individually, which limits the optimization efficiency and accuracy. To deal with this problem, the paper tries to improve production optimization performance by introducing reservoir engineering method into conventional particle swarm optimization (PSO) in three ways: the preprocessing result by reservoir engineering method is used respectively as population initialization, the search space constraint and the particle velocity guide item in PSO. Results show that all the three improved optimization methods can speed up the convergence rate of PSO algorithm while keeping similar convergence results at the same time. Furthermore, the use of the reservoir engineering preprocessing scheme as the search space constraint obtains the best convergence performance and reduces the iteration calculations by 24.14%, providing an effective way to reduce calculation cost for reservoir production optimization in commercial oilfields.

关键词： Reservoir production optimization Reservoir engineering method particle swarm optimization algorithm Improved optimization method

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particle swarm optimization algorithm for Neuro-Fuzzy Prospectivity Analysis Using Continuously Weighted Spatial Exploration Data

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NATURAL RESOURCES RESEARCH 2019年第2期28卷 309-325页

作者： Roshanravan, Bijan Aghajani, Hamid Yousefi, Mahyar Kreuzer, Oliver Shahrood Univ Technol Fac Min Petr & Geophys Shahrood *** Iran Malayer Univ Fac Engn Malayer Iran Corp Geosci Grp POB 5128 Rockingham Beach WA 6969 Australia James Cook Univ Sch Earth & Environm Sci EGRU Townsville Qld 4811 Australia

Classification of spatial exploration data for exploration targeting using neuro-fuzzy models means that the many spatial values have to be simplified and assigned to a few classes. The simplification of complex geological information, which illustrates a high degree of variability, results in overly simplistic models based on the presumption of homogeneous earth. However, such an assumption is not valid. In this paper, we illustrate the superiority of using continuously weighted spatial evidence values compared to discretely weighted evidence data, and how continuously weighted spatial evidence values can increase the efficiency of neuro-fuzzy exploration targeting models. The results of this study demonstrate that neuro-fuzzy targeting model generated with continuously weighted spatial evidence values is superior to that of the neuro-fuzzy model generated with discretely weighted exploration evidence data.

关键词： Continuous weighting Exploration targeting Neuro-fuzzy particle swarm optimization algorithm

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optimization of dividing wall columns based on online Kriging model and improved particle swarm optimization algorithm

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COMPUTERS & CHEMICAL ENGINEERING 2022年 166卷

作者： Liang, Mengkun Song, Jiayin Zhao, Kefan Jia, Shengkun Qian, Xing Yuan, Xigang Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing 100029 Peoples R China Beijing Univ Chem Technol Sch Econ & Management Beijing 100029 Peoples R China Tianjin Univ Chem Engn Res Ctr Collaborat Innovat Ctr Chem Sci & Engn Tianjin Sch Chem Engn & Technol Tianjin 300350 Peoples R China Zhejiang SUPCON Technol Co Ltd Hangzhou 310053 Peoples R China

Dividing wall columns (DWCs) can effectively improve the thermodynamic efficiency of traditional distillation columns. However, DWCs have intricate structures and strong internal interactions. Numerous structural and operational variables are interrelated. This work presents an improved cellular particle swarm optimization based on the online Kriging model (KCPSO) algorithm, and applies it to the optimization of DWC with the objective of minimizing the total annual cost. The algorithm uses the information of particle swarm search to act on the online Kriging model, and reacts on the particle search through the information of the online Kriging model. Calculations demonstrate that the KCPSO algorithm is superior to standard particle swarm optimization (PSO) and cellular PSO (CPSO) algorithms due to its higher quality of iteration. The KCPSO algorithm can effectively overcome the difficulty of early convergence of the CPSO algorithm and the problem that the PSO algorithm is prone to falling into local optimal solutions.

关键词： Heuristic algorithm Online surrogate model Optimal design Dividing wall columns particle swarm optimization algorithm

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A particle swarm optimization algorithm Based on Time-Space Weight for Helicopter Maritime Search and Rescue Decision-Making

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IEEE ACCESS 2020年 8卷 81526-81541页

作者： Chen, Zikun Liu, Hu Tian, Yongliang Wang, Rui Xiong, Peisen Wu, Guanghui Beihang Univ Sch Aeronaut Sci & Engn Beijing 100083 Peoples R China Commercial Aircraft Corp China Ltd Shanghai 200210 Peoples R China

One of the important problems to be solved in maritime search and rescue (MSAR) is decision-making, and the premise of it is determining the mission area for search and rescue unit. To solve the problem that classical cellular iterative search (CIS) algorithm is easy to fall into local optimal solution when determining the mission area, the particle swarm optimization algorithm based on time-space weight (TS-PSO) is proposed in this paper. This algorithm summarizes the optimization objectives and constraint conditions of the MSAR mission area planning according to search theory, carries out the parametric modeling of mission area legitimately and obtains the global optimal solution by continuous exploration in the parameter definition domain. On this basis, by analyzing the time-space weight of drift prediction data, the optimization results are further improved. Finally, through the case simulation analysis, it can be seen that the TS-PSO algorithm can effectively make up for the deficiency of the CIS algorithm and further improve the success probability of optimal MSAR mission area.

关键词： Global optimal solution maritime search and rescue mission area planning particle swarm optimization algorithm time-space weight

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particle swarm optimization algorithm-Based Design Method for Ultrasonic Transducers

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MICROMACHINES 2020年第8期11卷 715-715页

作者： Chen, Dongdong Zhao, Jianxin Fei, Chunlong Li, Di Zhu, Yuanbo Li, Zhaoxi Guo, Rong Lou, Lifei Feng, Wei Yang, Yintang Xidian Univ Sch Microelect Xian 710071 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Peoples R China

In order to improve the fabrication efficiency and performance of an ultrasonic transducer (UT), a particle swarm optimization (PSO) algorithm-based design method was established and combined with an electrically equivalent circuit model. The relationship between the design and performance parameters of the UT is described by an electrically equivalent circuit model. Optimality criteria were established according to the desired performance;then, the design parameters were iteratively optimized using a PSO algorithm. The Pb(ZrxTi1-x)O-3(PZT) ceramic UT was designed by the proposed method to verify its effectiveness. A center frequency of 6 MHz and a bandwidth of -6 dB (70%) were the desired performance characteristics. The optimized thicknesses of the piezoelectric and matching layers were 255 mu m and 102 mu m. The experimental results agree with those determined by the equivalent circuit model, and the center frequency and -6 dB bandwidth of the fabricated UT were 6.3 MHz and 68.25%, respectively, which verifies the effectiveness of the developed optimization design method.

关键词： ultrasonic transducer equivalent circuit model optimization design particle swarm optimization algorithm

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A precise ultra-wideband ranging method using pre-corrected strategy and particle swarm optimization algorithm

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MEASUREMENT 2022年 194卷

作者： Li, Di Wang, Xianglong Chen, Dongdong Zhang, Qidong Yang, Yintang Xidian Univ Sch Microelect Xian 710071 Peoples R China

In order to improve the ultra-wideband (UWB) ranging accuracy, a precise UWB ranging method is developed using pre-corrected strategy and particle swarm optimization (PSO) algorithm. In the pre-corrected strategy, the antenna delay function is established to correct the antenna parameters. In addition, the parameters of Kalman filter function are optimized by PSO algorithm to further improve the UWB ranging accuracy. The experimental results show that the maximum and average errors of the developed method are 7.45 and 3.82 cm, which are much smaller than those of the traditional ranging method (18 and 9.75 cm). Therefore, the developed UWB ranging method can be used in the precise ranging of electric devices.

关键词： Ultra-Wideband (UWB) Ranging method Pre-corrected strategy particle swarm optimization algorithm

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Optical watermark scheme based on singular value decomposition ghost imaging and particle swarm optimization algorithm

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JOURNAL OF MODERN OPTICS 2020年第12期67卷 1059-1071页

作者： Wu, Jun-Yun Huang, Wei-Liang Zuo, Mei-Juan Gong, Li-Hua Nanchang Univ Dept Comp Sci & Technol Nanchang Jiangxi Peoples R China Nanchang Univ Dept Elect Informat Engn Nanchang 330031 Jiangxi Peoples R China

An optical image watermarking algorithm, based on singular value decomposition (SVD) ghost imaging and multiple transforms, is designed. The watermark image is first encrypted by applying an SVD ghost imaging system, then the encrypted watermark is embedded into the cover image with the help of multiple transforms, including lifting wavelet transform (LWT), discrete cosine transform (DCT), discrete fractional angular transform (DFAT) and SVD. Four sub-band images are produced from the host image by LWT and DCT. The improved DFAT, whose scaling factors and parameter are optimized by particle swarm optimization algorithm, is operated in the new matrix. Afterwards, SVD is executed in the two-part image and the encrypted watermark is embedded in the host image by mutual operation of different matrices. Simulation results validate that the proposed watermark scheme is superior in the aspects of security, robustness and imperceptibility.

关键词： Optical watermarking SVD ghost imaging lifting wavelet transform discrete fractional angular transform particle swarm optimization algorithm

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Research and Application of the Transient Electromagnetic Method Inversion Technique Based on particle swarm optimization algorithm

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IEEE ACCESS 2020年 8卷 198307-198316页

作者： Xu, Zhengyu Fu, Zhihong Zhang, Jing Chongqing Univ Sch Elect Engn State Key Lab Power Transmiss Equipment & Syst Se Chongqing 400044 Peoples R China Chongqing Triloop Prospecting Technol Co Ltd Chongqing 402660 Peoples R China

The transient electromagnetic (TEM) method is widely used in shallow surface engineering geological surveys due to its advantages such as light weight, high efficiency, and strong resolution. However, interpretation and inversion of TEM data is a complicated process. The traditional algorithm of TEM inversion employs the "smoke ring" fast imaging method, which can only reflect the approximate morphology of the stratigraphic model, and the inversion accuracy is low. Therefore, this method cannot meet the requirements of high-precision inversion. In this article, we present the particle swarm optimization (PSO) algorithm for TEM inversion. First of all, the response of the rectangular loop source TEM based on electric dipole integration was calculated and compared with the analytical solution results of the rectangular loop source and the accuracy of the algorithm was verified. Then, we introduced the solution process of the particle swarm optimization algorithm and analyzed the influence of particle swarm optimization algorithm parameter selection on the accuracy of the inversion result and the convergence speed. Next, a layered medium model was established. The particle swarm optimization algorithm and "smoke ring" fast imaging method were used to perform inversion calculation. The results show that the PSO algorithm has the advantages of high efficiency and accuracy. Finally, we examined the effectiveness of the particle swarm optimization algorithm for TEM data processing by inverting survey data from an Air-raid shelter on the campus of Chongqing University in China and comparing the results with those from the "smoke ring" fast imaging. The research works in this article provide new methods and techniques for TEM data processing.

关键词： particle swarm optimization Transient analysis Imaging Conductivity Convergence Inverse problems Transient electromagnetic method particle swarm optimization algorithm fast imaging inversion

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Improved particle swarm optimization algorithm based novel encoding and decoding schemes for flexible job shop scheduling problem

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COMPUTERS & OPERATIONS RESEARCH 2020年 121卷

作者： Ding, Haojie Gu, Xingsheng East China Univ Sci & Technol Key Lab Adv Control & Optimizat Chem Proc Minist Educ Shanghai 200237 Peoples R China

The flexible job shop scheduling problem (FJSP) is a typical scheduling problem in practical production and has been proven to be a NP-hard problem. The study of FJSP is important to remarkably direct actual manufacturing processes. The paper proposes an improved particle swarm optimization (PSO) algorithm for solving FJSP and obtains beneficial solutions by improvement on encoding/decoding scheme, communication mechanism between particles, and alternate rules of candidate machines of operations. The innovation of encoding/decoding scheme proposes a novel designed chain encoding scheme and a corresponding effective decoding scheme. The chain-based encoding scheme can reasonably convert FJSP to an appropriate operation linked list and the novel designed decoding scheme owns the capacity of further explorering the solution space. The improvement of traditional PSO focuses on the innovation of information communication between particles, besides the modification of algorithm architecture. The amelioration of rules on operated machine selection is carried out based on the critical path of operations research (OR). It promotes algorithm efficiency by only alternating the candidate machines of operations on the critical path. In addition, much parameters tuning work is involved in a series of experiments. The study proposes some tuning schemes of parameters with exact mathematical methods, and these schemes can effectively help find more appropriate parameters. The final experiment results prove that the improved PSO exhibits remarkable ability to solve FJSP. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Flexible job shop scheduling problem particle swarm optimization algorithm Encoding and decoding schemes Local search Operations research

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